Roles and Responsibilities

• Build, lead and grow a cross-functional team of various engineering fields to deliver Bigdata/data Analytic / AI / ML-powered applications. 
• Be responsible for the solution architect of BigData/data Analytic project using agile methodology. 
• Understand and break down client problems, bringing an understanding of leading technology, analytics methods, tools, and operating model approaches
• Conduct research to provide technical solutions at scale for real-world challenges in various scenarios
• Help business stakeholders understand the potential and limitations of AI / ML when planning new products
• Work closely with product counterpart, taking accountability to deliver on the product strategy.
• Helps team make appropriate trade-offs based on quality, scalability, performance, and cost aligned with the team's tech strategy.
• Continuously evaluate relevant technologies, and influence architecture and design discussion.
• Work closely with the engineers to architect and develop the best technical design and approach.
• Report on the status of development, quality, operations, and system performance to management.
• Hold the team to a high standard of code quality, unit testing, architecture, tech debt management, code security, etc 

Desired Candidate Profile

• Bachelor's in an engineering or business discipline is a must.
• 10+ years of experience in developing, hosting, and maintaining large-scale distributed applications.
• 5+ years of experience in creating AI-enabled application solutions
• 3+ years in a people management role.
• Having experience in building and managing a Big Data analytics / AI engineering team is a must.
• Demonstrated experience in working on AI technologies e.g.: NLP, Virtual Agent, Conversational AI, NLU, NLG, forecasting, prediction, etc. 
• Working experience with the Technology stack available in the industry for data management, data ingestion, capture, processing, and curation: Kafka, HVR, Talend, Hadoop, Hive, Spark, etc.
• Hands-on experience in data analytics solutions using either AWS / Azure services: CloudFormation, Lake Formation, DMS, Kinesis, Kafka, CDC processing Redshift, RDS, Docker, Lambda, AI/ML, Azure data factory
• Knowledge of machine learning techniques clustering, decision tree learning, artificial neural networks, etc and their real-world advantages/drawbacks -Knowledge of advanced statistical techniques and concepts regression, properties of distributions, statistical tests, and proper usage, etc and experience with applications
• Knowledge of statistical computer languages R, Python, SLQ, etc to manipulate data and draw insights from large data sets

Key behavioral attributes

• Proactive and highly organized, with strong time management and planning skills
• Able to meet tight deadlines and remain calm under pressure
• Experience working with key stakeholders at senior levels. Demonstrable relationships with IT vendors are a plus.
• Strong Leadership, professional attitude – and leading by example
• Passionate about IT and a good understanding of emerging IT technologies are important
• Ability to multi-task and stay organized in a dynamic work environment
• Analytical and inquisitive, with excellent attention to detail
• Credible, confident, and articulate, with excellent communication and presentation skills and the
• gravitas to deliver ideas clearly and concisely to internal and external stakeholders
• Personable and approachable, with an enthusiastic and motivational nature and an overall
• passion for excellence