Gurgaon, India

Senior Data Scientist – AI/ML, GenAI & LLM Solutions Skills:Deep Learning, GenAI & LLM Solutions, Machine Learning & Advanced Analytics, NLP & Transformers, MLOps & Deployment, Cloud & Data Engineering Department: Data Science / AI EngineeringEmployment Type: Full TimeWork Mode: Onsite / HybridNotice Period: Immediate to 30 Days About the Role We are looking for a highly experienced Senior Data Scientist with deep expertise in Machine Learning, Generative AI, and Large Language Models (LLMs) to design and deploy production-grade AI systems. This role will focus on building scalable AI solutions such as LLM-powered applications, recommendation engines, forecasting models, and intelligent automation systems. You will collaborate with cross-functional teams to translate complex business problems into impactful AI-driven solutions and contribute to the overall AI strategy. Key Responsibilities GenAI & LLM Solutions Design and develop LLM-based applications using transformer models (GPT, LLaMA, Mistral, etc.) Build and deploy Retrieval-Augmented Generation (RAG) pipelines with vector databases Develop multi-agent AI systems using orchestration frameworks (LangChain / LangGraph) Implement advanced prompt engineering and optimization strategies Ensure model safety, guardrails, and output reliability for enterprise-grade deployments Machine Learning & Advanced Analytics Develop ML models for: Forecasting Recommendation systems Fraud detection Customer analytics and churn prediction Perform feature engineering, model tuning, and performance optimization Conduct statistical analysis, A/B testing, and hypothesis validation Work with structured and unstructured large-scale datasets NLP & Deep Learning Build NLP pipelines using transformers, embeddings, and language models Implement use cases like: Sentiment analysis Topic modeling Document intelligence Develop deep learning models (CNN, RNN, LSTM) using modern frameworks MLOps & Deployment Manage end-to-end ML lifecycle (training → validation → deployment → monitoring) Implement CI/CD pipelines for ML and GenAI systems Deploy models via APIs and scalable endpoints Ensure performance monitoring, observability, and continuous improvement Cloud & Data Engineering Work with cloud platforms (AWS / Azure / GCP) for scalable deployments Build data pipelines and data processing systems using SQL, PySpark, etc. Develop APIs and dashboards using frameworks like Flask / FastAPI / Streamlit Handle large-scale distributed datasets and optimize data workflows Required Skills & Experience Experience: 6–12 years in Data Science, AI/ML, and GenAI Strong programming skills in Python, SQL Expertise in ML frameworks: scikit-learn, XGBoost, TensorFlow, PyTorch Hands-on experience with: LLMs, GenAI frameworks, and RAG architectures NLP and transformer-based models Experience with MLOps tools, model deployment, and CI/CD pipelines Strong understanding of statistics, data modeling, and experimentation Experience working with cloud platforms and distributed systems What We Look For Strong analytical and problem-solving mindset Proven experience in deploying scalable, production-grade AI systems Ability to translate business requirements into data-driven solutions Experience working in fast-paced, high-growth environments Strong collaboration and stakeholder management skills Leadership ability, including mentoring junior team members Preferred Qualifications Bachelor’s/Master’s degree in Computer Science, Data Science, AI, or related field Certifications in Machine Learning, Deep Learning, or Generative AI Experience building enterprise-grade AI/LLM applications Exposure to multi-modal AI, agentic systems, or GenAI platforms Why This Role is High Impact Work on cutting-edge GenAI and LLM-driven systems Opportunity to build scalable, real-world AI products Direct influence on product innovation and business outcomes High ownership with strong growth and leadership opportunities #DataScience #AIJobs #GenAI #MachineLearning #LLM #HiringNow #TechCareers #AIEngineering #NLP #DeepLearning #MLOps #CloudComputing #AnalyticsJobs #CareersInAI

Machine Learning Engineer – Personalization, Recommendation Systems & AI Platforms Skills:Machine Learning | Recommendation Systems | Personalization | Deep Learning | MLOps | Distributed Systems | Real-time Inference | GenAI Department: AI / Machine Learning EngineeringEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 6–12 Years About the Role We are looking for a highly skilled Machine Learning Engineer to design and build scalable, production-grade ML systems powering personalization, recommendations, and intelligent user experiences at scale. This role sits at the intersection of machine learning, backend engineering, and large-scale distributed systems, focusing on delivering real-time, low-latency AI solutions that impact millions of users. You will work closely with cross-functional teams to translate business objectives into robust ML systems, driving innovation in AI-driven personalization and decisioning platforms. What You’ll Do Machine Learning & Personalization Systems Design and develop advanced recommendation systems (ranking, retrieval, collaborative filtering, embeddings, deep learning models) Build personalization engines for dynamic user-specific content delivery Develop models for real-time decisioning and prediction systems End-to-End ML Lifecycle Ownership Own the complete ML lifecycle: Problem definition Data exploration Model development Deployment Monitoring and optimization Continuously improve model performance through experimentation and iteration Scalable Systems & Real-Time Inference Build low-latency, high-throughput ML systems Design infrastructure to support real-time personalization at scale Optimize systems for performance, reliability, and scalability MLOps & Production Excellence Implement model versioning, monitoring, and retraining pipelines Build CI/CD pipelines for ML systems Ensure observability, governance, and reliability of ML deployments Data Engineering & Distributed Systems Design and manage large-scale data pipelines using distributed systems (Spark, Hadoop) Process and analyze massive datasets efficiently Optimize data workflows for ML use cases Cross-Functional Collaboration Work with product managers, data scientists, and engineers Translate business requirements into scalable ML solutions Drive innovation through collaboration and experimentation Innovation & Emerging Technologies Stay updated with advancements in: AI/ML and Deep Learning Generative AI and LLMs AI agents and automation systems Bring cutting-edge ideas into real-world production systems Must-Have Qualifications Experience: 6–12 years in Machine Learning Engineering or Backend Engineering Strong expertise in recommendation systems and personalization models Solid understanding of ML algorithms such as collaborative filtering, ranking models, embeddings, deep learning, and reinforcement learning Hands-on experience with TensorFlow, PyTorch, or Scikit-learn Strong programming skills in Python, Java, or Scala Experience with distributed systems such as Apache Spark and Hadoop Strong understanding of MLOps practices including deployment, monitoring, and lifecycle management Strong analytical and problem-solving skills Good-to-Have Experience with real-time inference systems and low-latency pipelines Exposure to Generative AI, LLMs, or AI agents Knowledge of MapReduce paradigms and large-scale data optimization Experience with A/B testing and experimentation platforms What We Look For Strong engineering mindset with ML system design expertise Ability to build production-grade scalable AI solutions Passion for solving high-scale, real-time problems Strong collaboration and communication skills Ability to thrive in fast-paced, innovation-driven environments Why This Role is High Impact Build AI systems that impact millions of users in real time Work on advanced personalization and recommendation engines Solve complex large-scale engineering and ML challenges Contribute to next-generation AI-powered digital experiences #MachineLearning #MLEngineer #AIJobs #RecommendationSystems #Personalization #MLOps #DeepLearning #DataEngineering #GenAI #LLM #HiringNow #TechCareers #AIEngineering #BigData #CloudComputing

Cloud Data Engineer – Cloud Platforms, ETL/ELT & Big Data Systems Skills:Cloud Data Engineering | AWS | Azure | GCP | ETL/ELT | Big Data | Data Warehousing | Data Pipelines | Data Governance Department: Data Engineering / Cloud PlatformEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 5–10 Years About the Role We are seeking a highly skilled Cloud Data Engineer to design, build, and maintain scalable, high-performance data pipelines across cloud platforms. This role focuses on enabling data-driven decision-making by ensuring efficient data ingestion, transformation, storage, and accessibility for analytics and business intelligence. You will work with large volumes of structured and unstructured data, contributing to enterprise-scale data architecture and cloud transformation initiatives. Key Responsibilities Data Pipeline Development & Optimization Design, build, and maintain scalable ETL/ELT pipelines across cloud environments Process large-scale structured and unstructured datasets efficiently Optimize pipelines for performance, reliability, and cost efficiency Cloud Data Architecture Develop and manage cloud-native data solutions using AWS, Azure, or GCP Work with data warehouses such as Redshift, BigQuery, Snowflake, or Synapse Design and implement data lake and data warehouse architectures Data Engineering & Processing Build robust data workflows using Python, SQL, and Spark (PySpark) Develop batch and real-time data processing pipelines Ensure high data quality, integrity, and consistency across systems Data Orchestration & Automation Implement orchestration workflows using Apache Airflow or similar tools Automate data ingestion, transformation, and deployment pipelines Support CI/CD practices for data engineering workflows Monitoring, Performance & Troubleshooting Monitor pipeline performance and resolve bottlenecks Optimize query performance and data processing efficiency Ensure system reliability through proactive issue resolution Security, Compliance & Governance Implement data security, encryption, and access control mechanisms Ensure compliance with data privacy and regulatory standards Support data governance, metadata management, and auditing processes Collaboration & Cross-Functional Work Collaborate with data scientists, analysts, and engineering teams Enable data access for analytics, reporting, and machine learning use cases Support enterprise-wide data initiatives and platform integrations Cloud Migration & Innovation Support cloud migration and modernization initiatives Evaluate new tools and technologies for data engineering improvements Contribute to proof-of-concept (POC) and architecture design decisions Technical Skills Programming & Data Processing Required: Python, SQL Preferred: PySpark, Scala, Java Databases & Data Management Relational databases: PostgreSQL, MySQL, SQL Server Cloud data warehouses: Redshift, BigQuery, Snowflake, Synapse Experience with data lake architectures Cloud Technologies AWS, Azure, or GCP Cloud-native storage, compute, and data services Frameworks & Tools Apache Spark, Hadoop ecosystem Data integration tools (Apache NiFi or similar) Orchestration & DevOps Airflow, Terraform, CloudFormation Jenkins, Git, Docker, Kubernetes Security & Compliance Data masking, encryption, IAM Compliance frameworks (GDPR, HIPAA, or equivalent) Experience Requirements 5+ years of experience in cloud data engineering and pipeline development Proven experience with ETL/ELT processes and large-scale data systems Hands-on experience with cloud data platforms and warehouses Experience optimizing data workflows for scalability and performance Exposure to cloud migration and infrastructure automation preferred Day-to-Day Responsibilities Design and implement scalable data pipelines Collaborate with cross-functional teams on data workflows Automate ingestion and transformation processes Troubleshoot pipeline issues and optimize performance Ensure data security and governance compliance Support cloud migration and modernization projects Maintain documentation and architecture diagrams Qualifications Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field Certifications in cloud data platforms (AWS / GCP / Azure) preferred Experience working in enterprise-scale or regulated environments is a plus Professional Competencies Strong analytical and problem-solving skills Excellent communication and collaboration abilities Ability to manage multiple priorities in fast-paced environments Leadership mindset with mentoring capabilities Strategic thinking for scalable and secure data architecture Continuous learning mindset with adaptability to new technologies Why This Role is High Impact Work on large-scale cloud data platforms and enterprise systems Enable data-driven decision-making across business functions Build high-performance, scalable data infrastructure Contribute to digital transformation and modernization initiatives #CloudDataEngineer #DataEngineering #BigData #ETL #DataPipelines #AWS #Azure #GCP #DataWarehousing #Spark #Airflow #DataGovernance #CloudComputing #TechCareers #HiringNow

AI Research Scientist – Deep Tech / Healthcare AI (GenAI, LLMs, Multimodal AI) Skills:Artificial Intelligence | Generative AI | Large Language Models (LLMs) | Small Language Models (SLMs) | NLP | Deep Learning | Multimodal AI | Healthcare AI | Model Optimization | Research & Experimentation Department: AI Research / Advanced AnalyticsEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 4–10 Years About the Role We are seeking a highly skilled AI Research Scientist to drive cutting-edge research and development in Generative AI, Large Language Models (LLMs), and domain-specific AI systems, particularly within deep-tech and healthcare AI applications. This role involves designing and optimizing domain-adapted AI models to solve complex real-world problems across areas such as clinical intelligence, diagnostics, medical NLP, and multimodal healthcare data processing. You will work at the intersection of research and production, contributing to the development of scalable, safe, and high-performance AI systems. Key Responsibilities AI Research & Model Development Conduct advanced research in NLP, deep learning, and multimodal AI Design and develop domain-specific LLMs and SLMs for healthcare and deep-tech use cases Work on foundation model training, fine-tuning, and optimization Develop novel approaches to improve model performance, robustness, and efficiency Generative AI & LLM Engineering Build and optimize LLM-based applications using transformer architectures Implement: Supervised fine-tuning Reinforcement Learning from Human Feedback (RLHF) Pre-training and post-training optimization techniques Apply techniques such as LoRA, QLoRA, and parameter-efficient fine-tuning (PEFT) Design and deploy RAG (Retrieval-Augmented Generation) pipelines Multimodal & Healthcare AI Systems Develop AI systems integrating: Text (clinical notes, reports) Imaging (radiology, pathology) Structured medical data Build models for: Clinical decision support Diagnostic insights Medical knowledge extraction Ensure models meet clinical-grade reliability and compliance standards Data Engineering & Model Training Process and curate large-scale healthcare datasets Design data pipelines for: Data ingestion Preprocessing Feature engineering Ensure high-quality training data and bias mitigation Model Evaluation & Optimization Evaluate models using domain-specific metrics and benchmarks Perform: Model validation Error analysis Continuous optimization Improve model safety, interpretability, and explainability Collaboration & Production Integration Work with cross-functional teams including: Data engineers Product teams Domain experts (clinical/research) Translate research into production-ready AI solutions Support deployment and scaling of AI models Research & Thought Leadership Publish research in top-tier conferences and journals Stay updated with advancements in: AI/ML NLP and LLMs Healthcare AI Contribute to innovation through experimentation and prototyping Required Qualifications Bachelor’s/Master’s/PhD in Computer Science, AI, Data Science, or related field 4–10 years of experience in AI/ML research and development Strong expertise in: NLP, deep learning, and transformer architectures LLMs, SLMs, and generative AI systems Experience with: Fine-tuning techniques (full fine-tuning, PEFT, LoRA, QLoRA) RLHF and model alignment methods Proficiency in: Python ML frameworks (PyTorch, TensorFlow) Hugging Face ecosystem Experience working with: Large-scale datasets Distributed training systems Technical Skills AI/ML & Deep Learning NLP, transformers, embeddings, multimodal models Model training, evaluation, and optimization GenAI & LLM Frameworks LangChain, LangGraph, LlamaIndex, LangSmith RAG pipelines and vector databases Programming & Data Python, SQL Data processing frameworks (PySpark, Pandas) Cloud & Infrastructure AWS / Azure / GCP Model deployment and scalable inference systems Responsible AI & Compliance Model safety, fairness, and bias mitigation Understanding of regulatory requirements in healthcare (preferred) Good-to-Have Experience in healthcare AI, clinical data, or medical imaging Exposure to multimodal AI systems Knowledge of AI governance and regulatory frameworks Contributions to open-source or published research work Experience deploying AI systems in production environments Professional Competencies Strong research mindset with problem-solving capabilities Ability to translate complex problems into AI solutions Strong communication skills for technical and non-technical audiences Ability to work in cross-functional and collaborative environments High ownership and innovation-driven thinking Adaptability to evolving technologies and research trends Why This Role is High Impact Work on cutting-edge AI research and real-world healthcare applications Build next-generation GenAI and LLM systems Contribute to high-impact innovations in healthcare and deep-tech Opportunity to publish, innovate, and influence AI strategy Solve complex problems with large-scale, high-value data systems #AIResearch #GenAI #LLM #DeepLearning #HealthcareAI #NLP #MachineLearning #MultimodalAI #AIInnovation #DataScience #AIJobs #ResearchCareers #ArtificialIntelligence #HiringNow

Backend Engineer – Java / Python / Microservices Architecture Skills:Backend Development | Java | Python | Microservices | Distributed Systems | APIs | Cloud Platforms | System Design | Data Platforms Department: Software Engineering / Backend PlatformEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 5–10 Years About the Role We are seeking a highly skilled Backend Engineer to design, build, and scale robust, high-performance backend systems and microservices architectures. This role involves working on enterprise-grade platforms that handle large-scale data processing, system integrations, and real-time transactions. You will play a key role in developing scalable APIs, distributed systems, and backend services, contributing to critical business applications and platform capabilities. Key Responsibilities Backend Development & System Design Design and develop scalable backend services and APIs using Java (Spring Boot) or Python frameworks Build and maintain microservices-based architectures Ensure high performance, scalability, and reliability of backend systems Develop reusable components and services for enterprise applications Software Development Lifecycle (SDLC) Participate in all stages of SDLC: Requirements analysis System design Development and testing Deployment and maintenance Follow best practices for code quality, version control, and documentation Contribute to code reviews and architectural discussions Distributed Systems & Scalability Design and optimize distributed systems handling large-scale workloads Build fault-tolerant, highly available applications Ensure system performance under high concurrency and load API Development & Integration Develop RESTful APIs and integrate with internal/external systems Ensure API security, scalability, and maintainability Support seamless integration across platforms and services Data Engineering & Storage Work with relational and NoSQL databases (SQL, MongoDB, etc.) Design efficient data models and optimize database queries Handle large datasets and ensure data consistency and integrity Cloud & Infrastructure Develop and deploy backend services on cloud platforms (AWS, GCP, Azure) Work with containerization and orchestration tools (Docker, Kubernetes) Support CI/CD pipelines and infrastructure automation Collaboration & Cross-Functional Work Collaborate with product managers, frontend engineers, and architects Participate in design discussions to define system architecture Translate business requirements into scalable technical solutions Monitoring, Debugging & Optimization Monitor system performance and troubleshoot production issues Optimize application performance and reduce latency Ensure system observability and reliability Required Qualifications Bachelor’s or Master’s degree in Computer Science or related field 5–10 years of experience in backend development Strong expertise in: Java (Spring Boot) or Python frameworks Microservices architecture and distributed systems Experience with: REST APIs and backend service development SQL and NoSQL databases Strong understanding of: System design and scalability principles Software engineering best practices Technical Skills Programming Languages Java, Python Preferred: Scala, Go Frameworks & Technologies Spring Boot, Django, Flask Microservices architecture Databases Relational: PostgreSQL, MySQL NoSQL: MongoDB, Cassandra Cloud & DevOps AWS, GCP, Azure Docker, Kubernetes CI/CD tools (Jenkins, Git) Architecture & Systems Distributed systems Event-driven architecture API gateways and service mesh Good-to-Have Experience with data governance, data platforms, or identity systems Exposure to AI/ML-based backend systems Knowledge of security and compliance frameworks Experience working in financial services or high-scale platforms Professional Competencies Strong analytical and problem-solving skills Ability to work in fast-paced, high-scale environments Strong communication and collaboration skills Ownership mindset with attention to detail Ability to drive innovation and challenge existing systems Adaptability to evolving technologies and requirements Why This Role is High Impact Build high-scale backend systems used in critical applications Work on complex distributed architectures and real-time systems Contribute to platform scalability, performance, and innovation Opportunity to influence architecture and engineering best practices #BackendEngineer #Java #Python #Microservices #SystemDesign #DistributedSystems #APIs #CloudComputing #SoftwareEngineering #TechCareers #HiringNow #ScalableSystems #DevOps #EngineeringJobs

Senior Engineer – Cloud Infrastructure (AWS, Automation, AI-Driven Operations) Skills:Cloud Infrastructure | AWS | Infrastructure as Code | DevOps | Automation | Distributed Systems | AI/ML in Infra | Observability | Security & Compliance Department: Cloud Infrastructure / Platform EngineeringEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 3–8 Years About the Role We are seeking a highly skilled Senior Engineer – Cloud Infrastructure to design, build, and operate scalable, secure, and highly available cloud platforms, primarily on AWS. This role focuses on automation-first infrastructure engineering, enabling teams to build and deploy applications efficiently while maintaining high standards of security, reliability, and cost optimization. A key aspect of this role involves leveraging AI/ML and agent-based systems to automate infrastructure workflows, incident response, and operational processes, improving system efficiency and reducing manual intervention. Key Responsibilities Cloud Infrastructure Engineering (AWS) Design, implement, and manage highly available and scalable AWS infrastructure Work with core services including: VPC, networking, and routing EC2, Auto Scaling, Load Balancers S3, EBS/EFS/FSx storage systems IAM, KMS, and security services Ensure infrastructure is secure, resilient, and optimized for performance and cost Infrastructure as Code & Automation Develop and maintain Infrastructure-as-Code (IaC) using tools such as: AWS CDK CloudFormation Terraform Build automation tools using Python or TypeScript Eliminate manual processes through automation of provisioning, patching, compliance, and reporting AI-Driven Infrastructure & Agentic Systems Identify opportunities to automate infrastructure workflows using AI/ML and agent-based systems Design and implement single-agent and multi-agent workflows for: Incident triage Runbook automation Change impact analysis Cost and capacity optimization Integrate AI agents with: Cloud APIs Monitoring and observability tools Ticketing systems and workflows Implement guardrails for safety, compliance, and reliability in AI-driven operations Reliability, Monitoring & Operations Own infrastructure reliability across environments Implement monitoring and observability using tools such as: CloudWatch Datadog Splunk Define and manage SLOs, SLAs, and alerting systems Participate in on-call rotations and incident management Conduct root cause analysis and drive continuous improvement initiatives Security & Compliance Implement security best practices across cloud infrastructure Work with IAM policies, encryption (KMS), and network security controls Ensure compliance with organizational and regulatory standards Collaborate with security teams for audits and governance Collaboration & Architecture Collaborate with: SRE teams Security engineering Product engineering teams Participate in architecture discussions, design reviews, and technical planning Contribute to standards, best practices, and reusable infrastructure patterns Mentoring & Knowledge Sharing Mentor junior engineers on: Cloud infrastructure fundamentals Automation best practices AI-driven operations Contribute to: Documentation Runbooks Knowledge base articles Lead internal training sessions on cloud and automation practices Required Qualifications 3–8 years of experience in Cloud Infrastructure / DevOps / Platform Engineering Strong hands-on experience with AWS cloud services Deep understanding of: Networking (VPC, subnets, routing, VPN, security groups) Compute and storage services Identity and access management (IAM) Experience with Infrastructure-as-Code tools (CDK, Terraform, CloudFormation) Strong programming skills in Python or TypeScript Experience building and managing production-grade cloud environments Knowledge of monitoring, logging, and observability practices AI / Automation Skills (Required) Experience working with LLM-based or AI-driven automation systems Hands-on exposure to: AI agent frameworks or orchestration tools Multi-step workflow automation using APIs and function calling Understanding of: Prompt engineering Retrieval-Augmented Generation (RAG) AI safety and output validation Technical Skills Cloud Platforms AWS (primary) Exposure to Azure or GCP is a plus Infrastructure & DevOps Terraform, AWS CDK, CloudFormation CI/CD pipelines, Git workflows Programming Python TypeScript / Node.js Observability CloudWatch, Datadog, Splunk Logging, metrics, tracing, alerting Security IAM, encryption, network security Compliance and governance frameworks Good-to-Have Experience in large-scale SaaS or multi-tenant environments Knowledge of FinOps and cost optimization strategies Experience integrating AI agents with: Ticketing systems (Jira, ServiceNow) Collaboration tools (Slack, Teams) AWS certifications (Solutions Architect, Security, Networking) Professional Competencies Strong problem-solving and analytical skills Ability to manage complex infrastructure projects end-to-end Strong collaboration and stakeholder management Leadership and mentoring capabilities Adaptability in fast-changing environments Focus on innovation, automation, and continuous improvement Why This Role is High Impact Build and scale enterprise-grade cloud infrastructure platforms Drive automation-first and AI-driven infrastructure operations Work on high-availability, large-scale distributed systems Influence cloud architecture and engineering best practices Contribute to next-generation infrastructure innovation #CloudInfrastructure #AWS #DevOps #PlatformEngineering #InfrastructureAsCode #Automation #AIinInfra #MLOps #DistributedSystems #SRE #CloudEngineering #TechCareers #HiringNow

Product Manager – AI / Fintech / SaaS (Data-Driven, Platform & Growth Focus) Skills:Product Management | AI/ML Products | Fintech | SaaS | Data Analytics | SQL | APIs | Customer Experience | Growth & Engagement Department: Product ManagementEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 4–10 Years About the Role We are looking for a highly analytical and execution-focused Product Manager who thrives on solving complex problems using data and first-principles thinking. This role sits at the intersection of product, data, technology, and business, and is ideal for someone who is comfortable diving deep into metrics, questioning assumptions, and driving meaningful product outcomes. You will work closely with engineering, analytics, design, business stakeholders, and external partners to identify opportunities, validate hypotheses using data, and deliver high-impact product solutions across AI-driven platforms, fintech systems, and SaaS products. Who is an Ideal Candidate Data-Driven Decision Maker Strong ability to analyze large datasets and extract actionable insights Comfortable writing complex SQL queries, building dashboards, and validating metrics Ability to question data quality and work with teams to fix gaps in data pipelines AI-First Product Thinking Thinks AI-first when solving problems Identifies opportunities to leverage AI/ML, automation, and personalization Experience or strong interest in building AI-driven product features and workflows D2C / SaaS Product Experience Experience building and scaling consumer or SaaS products Strong focus on: User acquisition Engagement Retention Proven ability to deliver features with measurable business impact Customer-Centric Approach Deep understanding of customer needs through data, research, and feedback loops Continuously improves user experience and key engagement metrics Ability to translate user pain points into product solutions Strong Execution & Ownership Own product delivery end-to-end: Define product requirements Manage product backlog Prioritize features Drive execution with engineering teams Write clear user stories and define acceptance criteria Ensure timely delivery through sprint planning and execution Cross-Functional Collaboration Act as a bridge between: Engineering Business Analytics Design teams Translate business requirements into technical solutions Balance trade-offs between speed, quality, and scalability Key Responsibilities Product Strategy & Roadmap Define product vision and roadmap aligned with business goals Identify opportunities for product innovation and growth Prioritize features based on impact, feasibility, and data insights Data Analysis & Experimentation Analyze product performance using: SQL Dashboards Analytics tools Design and run A/B experiments to validate product decisions Drive continuous improvement through data-driven iteration AI / Fintech / SaaS Product Development Build and scale products across: AI/ML platforms Fintech systems (payments, lending, risk, etc.) SaaS applications Collaborate with engineering teams on: APIs Backend systems Data validation Execution & Delivery Manage Agile/Scrum processes and sprint cycles Ensure clear communication of priorities and timelines Track progress and resolve blockers proactively Stakeholder Management Collaborate with internal and external stakeholders Communicate product updates, progress, and outcomes Align cross-functional teams on product goals What We’re Looking For 4–10 years of experience in Product Management Experience in Fintech, SaaS, or AI-driven products preferred Strong understanding of: Product lifecycle User experience design Data analytics Hands-on experience working with: Engineering teams APIs and backend systems Strong problem-solving and analytical thinking Technical & Functional Skills Product & Analytics SQL, dashboards, data analysis tools A/B testing and experimentation frameworks Technology Understanding APIs, backend systems, and system architecture Basic understanding of AI/ML concepts Methodologies Agile / Scrum Product lifecycle management Professional Competencies Strong communication and storytelling skills Ability to simplify complex ideas Independent thinking with a questioning mindset Strong ownership and accountability Ability to manage ambiguity and drive clarity Collaborative and team-oriented approach Why This Role is High Impact Work on high-growth AI, fintech, and SaaS products Drive data-driven product decisions and innovation Build products that impact customer experience and business growth Collaborate with cross-functional teams on cutting-edge solutions Opportunity to influence product strategy and roadmap #ProductManager #AIProducts #Fintech #SaaS #ProductManagement #DataDriven #SQL #A_BTesting #GrowthProduct #TechCareers #HiringNow #ProductJobs

Engineering Manager / Tech Lead – Platform, Product & Scalable Systems Skills:Engineering Leadership | System Design | Scalable Architectures | Backend Development | Cloud Platforms | Team Building | Agile Delivery | Stakeholder Management Department: Engineering / TechnologyEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 8–15 Years About the Role We are looking for a strong Engineering Manager / Tech Lead who can build, mentor, and scale high-performing engineering teams while driving delivery of robust, scalable, and high-impact technology solutions. This role combines technical depth with leadership excellence, requiring the ability to translate business problems into engineering outcomes, guide architectural decisions, and create an environment where teams can thrive and innovate. You will work closely with product, architecture, and business stakeholders to deliver solutions that create measurable impact while maintaining high standards of engineering quality and execution. What You’ll Do Engineering Leadership & Team Building Build, mentor, and scale high-performing engineering teams Foster a culture of ownership, accountability, and continuous learning Support career growth through coaching, feedback, and mentorship Create an environment that encourages innovation, experimentation, and collaboration Technical Strategy & System Design Drive architecture and system design decisions for scalable applications Ensure systems are designed for: Scalability Reliability Performance Provide technical guidance on: Backend systems Distributed architectures Cloud platforms Execution & Delivery Drive end-to-end delivery of engineering initiatives Focus on outcomes aligned with business goals, not just output Manage sprint planning, execution, and delivery timelines Identify risks and proactively resolve blockers Cross-Functional Collaboration Partner with: Product teams Architecture teams Business stakeholders Translate business requirements into technical solutions and trade-offs Ensure alignment between engineering efforts and product strategy Quality, Performance & Best Practices Establish and enforce engineering best practices Ensure high standards in: Code quality Testing Documentation Promote adoption of DevOps, CI/CD, and automation practices Innovation & Technology Leadership Stay updated with evolving technology landscapes Drive adoption of modern technologies such as: Cloud-native architectures AI/ML and emerging technologies Encourage teams to experiment and innovate What We Look For Leadership & Team Impact Proven track record of building strong engineering teams Ability to inspire trust, accountability, and collaboration Experience managing performance, growth, and team dynamics Technical Expertise Strong background in: Backend engineering Distributed systems System design Experience with cloud platforms (AWS, Azure, or GCP) Ability to review and guide complex technical decisions Business & Product Alignment Ability to connect engineering efforts to business value and outcomes Strong understanding of product lifecycle and customer impact Experience working closely with product and business teams Execution Excellence Strong project management and delivery skills Experience working in Agile/Scrum environments Ability to manage multiple priorities in fast-paced environments Key Responsibilities Lead engineering teams to deliver scalable, high-quality solutions Drive technical architecture and system design decisions Ensure timely and efficient delivery of product features Collaborate with cross-functional teams to align on goals Mentor engineers and build leadership within the team Improve engineering processes and operational efficiency Technical Skills Core Engineering Java, Python, or similar backend technologies Microservices architecture and distributed systems Cloud & DevOps AWS, Azure, or GCP CI/CD pipelines and DevOps practices Architecture System design Scalable and fault-tolerant systems Professional Competencies Strong leadership and people management skills Excellent communication and stakeholder management Strategic thinking with execution focus Ability to navigate ambiguity and evolving requirements Strong problem-solving and decision-making skills Ownership mindset with accountability for outcomes What You’ll Get Opportunity to grow as a technology and people leader Work on projects with real business and customer impact A culture focused on ownership, collaboration, and excellence Exposure to modern technologies and large-scale systems A supportive team environment that encourages growth and innovation Why This Role is High Impact Direct influence on team performance and engineering culture Ownership of critical technology decisions and outcomes Opportunity to shape scalable systems and engineering strategy High visibility across product, business, and leadership teams #EngineeringManager #TechLead #EngineeringLeadership #SystemDesign #CloudEngineering #DistributedSystems #TechCareers #HiringNow #LeadershipJobs #SoftwareEngineering

Senior Manager – Risk Management (Fraud Prevention & Detection) Skills:Fraud Risk Management | Fraud Detection | Risk Analytics | Financial Crime Prevention | Transaction Monitoring | AML | Data Analytics | Machine Learning | Regulatory Compliance Department: Risk & Compliance / Fraud ManagementEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 8–15 Years About the Role We are seeking an experienced Senior Manager – Risk Management (Fraud Prevention & Detection) who brings deep domain expertise in fraud risk along with strong analytical and consulting capabilities. This role requires the ability to translate complex data, technology, and analytics into measurable business outcomes, driving effective fraud prevention strategies across digital platforms and financial systems. You will lead initiatives focused on fraud detection, risk mitigation, and transaction monitoring, working closely with cross-functional teams to design scalable solutions that protect customers, reduce losses, and ensure regulatory compliance. Key Responsibilities Fraud Strategy & Risk Management Define and implement fraud prevention and detection strategies across products and channels Develop frameworks for: Transaction monitoring Fraud risk scoring Alert management and investigation Identify emerging fraud trends and design proactive mitigation strategies Drive continuous improvement in fraud detection accuracy and efficiency Fraud Detection & Analytics Build and manage rule-based and model-driven fraud detection systems Leverage advanced analytics and machine learning for: Fraud pattern recognition Anomaly detection Behavioral analytics Monitor key fraud metrics and optimize detection thresholds Analyze large datasets to identify fraud risks and operational gaps Technology & Platform Integration Work with engineering teams to design and implement fraud detection platforms and tools Integrate fraud systems with: Payment platforms Transaction systems Customer data platforms Ensure real-time fraud detection capabilities with low latency Risk Governance & Compliance Ensure adherence to: Regulatory requirements Internal risk and compliance policies Support audits, regulatory reporting, and compliance reviews Align fraud strategies with AML and financial crime prevention frameworks Stakeholder Management & Consulting Collaborate with: Product teams Engineering teams Compliance and legal teams Translate business requirements into risk solutions Provide strategic recommendations to senior leadership Act as a subject matter expert for fraud risk and prevention Team Leadership & Operations Lead and mentor fraud risk and analytics teams Oversee fraud operations including: Alert handling Investigations Escalations Drive operational efficiency and process improvements Build a high-performance, data-driven risk culture Performance Monitoring & Optimization Define and track KPIs such as: Fraud loss rates False positive rates Detection accuracy Continuously optimize fraud models and rules Implement feedback loops to improve system performance Required Qualifications Bachelor’s or Master’s degree in Finance, Analytics, Computer Science, or related field 8–15 years of experience in: Fraud risk management Financial crime prevention Risk analytics Strong domain expertise in: Payment fraud Digital fraud Transaction risk management Experience with: Fraud detection tools and platforms Data analytics and reporting Technical Skills Analytics & Data SQL, Python, or R Data visualization tools (Tableau, Power BI) Experience with large-scale data analysis Fraud & Risk Systems Transaction monitoring systems Rule engines and risk scoring systems Case management and investigation tools Machine Learning (Preferred) Fraud modeling and anomaly detection Supervised and unsupervised learning techniques Compliance & Regulations AML, KYC, and financial crime frameworks Regulatory standards and reporting requirements Professional Competencies Strong analytical and problem-solving skills Ability to translate data into actionable insights Strong stakeholder management and communication skills Leadership and team management capabilities Strategic thinking with execution focus Ability to work in fast-paced, high-risk environments Why This Role is High Impact Protect critical systems from fraud and financial risk Drive data-driven fraud prevention strategies Influence risk management and compliance frameworks Work on high-scale transaction systems and real-time detection Play a key role in safeguarding business and customer trust #RiskManagement #FraudDetection #FraudPrevention #FinancialCrime #AML #RiskAnalytics #Fintech #DataAnalytics #MachineLearning #Compliance #TechCareers #HiringNow

Payments Architect – Solution Architecture, Real-Time Payments & Banking Platforms Skills:Payments Architecture | Fintech Systems | Real-Time Payments | API-First Design | Microservices | Distributed Systems | Cloud Architecture | Security & Compliance Department: Architecture / Fintech / Payments EngineeringEmployment Type: Full TimeWork Mode: Onsite / Hybrid / RemoteExperience: 10–18 Years About the Role We are seeking an experienced Payments Architect to lead the design and architecture of scalable, secure, and resilient payment processing systems across global markets. This role requires deep expertise in payments, fintech, and corporate banking systems, along with the ability to translate complex business requirements into robust architecture solutions. You will play a critical role in shaping end-to-end payment ecosystems, including initiation, validation, routing, clearing, and settlement across multiple payment rails. You will collaborate closely with product, business, and engineering teams to define architecture strategies, ensure alignment with enterprise standards, and drive successful delivery of high-impact fintech solutions. Key Responsibilities Solution Architecture & Design Define and govern end-to-end architecture for payment processing systems Translate business requirements into scalable architecture blueprints and solution designs Design systems supporting: Real-time payments Cross-border transactions High-volume transaction processing Define integration patterns across payment platforms and banking systems Evaluate system impacts across existing applications and infrastructure Produce architecture documentation and design artifacts Payments Platform & Domain Expertise Provide deep expertise across: Payment initiation and orchestration Screening, routing, clearing, and settlement Design systems supporting: Corporate banking functions (payments, liquidity, trade finance) Virtual account management Understand and implement global payment schemes and standards Ensure compliance with regulatory and operational requirements Integration & Distributed Systems Design API-first, event-driven architectures for payment platforms Integrate with payment networks such as: SWIFT SEPA RTP / Instant payment systems Build scalable solutions using: Microservices architecture Messaging and streaming platforms (Kafka) Ensure high availability, fault tolerance, and performance Cloud & Platform Architecture Architect cloud-native solutions on AWS, Azure, or GCP Design containerized and scalable deployments using Kubernetes Implement API gateways, service mesh, and backend services Optimize systems for scalability, latency, and cost Security & Compliance Design secure payment systems with: Encryption and key management OAuth2 / JWT authentication Data protection and fraud prevention mechanisms Ensure compliance with financial regulations and standards Incorporate security best practices across architecture layers Stakeholder Engagement & Delivery Collaborate with: Business stakeholders Product teams Engineering and delivery teams Act as a bridge between business requirements and technical solutions Support delivery teams throughout the lifecycle: Design Development Testing Deployment Conduct architecture reviews and governance approvals Architecture Governance Ensure adherence to: Enterprise architecture standards Technology frameworks Security guidelines Define non-functional requirements: Scalability Performance Resilience Evaluate new technologies, tools, and vendor solutions Required Qualifications Bachelor’s or Master’s degree in Computer Science, Engineering, or related field 10+ years of experience in technology, with at least 5+ years in payments architecture Strong experience designing enterprise-scale payment systems Deep understanding of: Payment processing workflows Banking and financial systems Strong technical background in: Java (11+), Spring Boot Microservices architecture REST APIs Technical Skills Payments & Fintech Systems SWIFT, SEPA, RTP, FedNow, and other payment rails Payment gateways and transaction processing systems Architecture & Design Microservices and event-driven architectures Distributed systems and integration patterns Cloud & Infrastructure AWS, Azure, or GCP Kubernetes, containerization Data & Streaming Kafka or similar streaming platforms Distributed databases and caching systems (Redis) Security OAuth2, JWT, encryption, key management Regulatory compliance frameworks Professional Competencies Strong analytical and architectural thinking Ability to design scalable and secure solutions Excellent stakeholder communication and collaboration skills Ability to influence architecture decisions across teams Strong problem-solving and decision-making skills Experience working in Agile and distributed team environments Why This Role is High Impact Design and build next-generation payment systems at scale Work on real-time, high-volume transaction platforms Influence architecture strategy and fintech innovation Solve complex challenges in global payments and banking systems Collaborate across business and technology to deliver impactful solutions #PaymentsArchitect #Fintech #Payments #BankingTechnology #SolutionArchitecture #Microservices #CloudArchitecture #DistributedSystems #RealTimePayments #API #TechCareers #HiringNow