Information Technology & SoftwareAccounting & Finance
9 Sept
AI Staff Engineer
Bangalore & Mumbai, India
45 LPA Per Annum
AI Staff Engineer
Notice Period - Immediate to 30 days
About the Role
We are hiring a Staff Engineer to own the architecture, design, and delivery of AI-powered and Agentic features across company. This is not an ML research role - it is a product engineering role for someone who can take large language models, tool-use patterns, and agentic frameworks and ship them as reliable, production-grade features that financial operations teams depend on daily. This role works alongside Lead Engineers who run the engineering team’s day-to-day delivery; the Staff Engineer owns architecture, patterns, and technical direction across both AI features and the broader platform.
You will define how AI is integrated into: which workflows become agentic, how models interact with our domain data, how we build trust and safety into autonomous financial operations, and how we evolve the platform architecture to support these capabilities at scale.
This is a high-autonomy, high-impact role. You will work across the full stack - from prompt engineering and model orchestration to API design, data pipelines, and frontend integration - and collaborate closely with product, design, and domain experts to ship features that meaningfully change how our clients operate.
Tech Stack & Environment
You will work across the following stack. Deep expertise in every layer is not required — but you should be comfortable navigating a polyglot codebase and making architectural decisions that span these technologies.
Cloud
Microsoft Azure (App Services, Functions, Storage, Service Bus, Key Vault)
Backend
existing platform services in C# (Core)/ .NET/ Python
Frontend
Angular / TypeScript
Database
SQL Server
Architecture
Containerized microservices (Docker, Azure Container Apps / AKS) and Azure App Services
DevOps
Azure DevOps (CI/CD pipelines, repos, boards)
AI Tooling
Claude, Codex CLI and Cursor for agentic development workflows
Integrations
MCP servers, REST APIs, payment rails (NACHA, ISO 20022, SWIFT), accounting system APIs
Key Responsibilities
Agentic Architecture & System Design
Design and build the core agentic infrastructure for product: agent orchestration, tool-use frameworks, memory/context management, and guardrails for autonomous financial workflows.
Define the architecture for how LLMs interact with products domain model: invoices/contracts/purchase orders, approvals, vendor records, payment instructions, payment orchestration, accounting entries - safely and reliably.
Build and maintain MCP (Model Context Protocol) servers and integrations that expose products capabilities as tools for AI agents and external AI platforms used by our clients.
Design patterns for human-in-the-loop oversight, approval gates, and escalation paths in agentic financial workflows.
AI Feature Development
Lead development of AI-powered product features: intelligent invoice processing, automated approval routing, anomaly detection, natural-language querying of financial data, and predictive cash-flow analysis.
Build and iterate on prompt chains, retrieval-augmented generation (RAG) pipelines, and multi-step agent workflows tailored to financial operations.
Implement evaluation frameworks: automated testing for AI outputs, regression detection, quality scoring, and production monitoring for model-driven features.
Own the integration layer between LLM providers (Anthropic, OpenAI, etc.) and backend-model selection, fallback strategies, cost optimization, and latency management.
Technical Leadership
Set technical direction for AI/agentic development across the engineering team. Write RFCs, architectural decision records, and technical specifications.
Mentor engineers on AI integration patterns, prompt engineering, evaluation methodology, and safe deployment of model-driven features.
Establish engineering standards for AI features: testing practices, monitoring, incident response, and responsible AI guidelines specific to financial data.
Drive build-vs-buy decisions for AI tooling, frameworks, and infrastructure. Evaluate emerging tools and frameworks and make pragmatic adoption recommendations.
Cross-Functional Collaboration
Partner with product management to identify high-value AI use cases, scope MVPs, and define success criteria grounded in client outcomes.
Work with the implementation team to understand client workflows and pain points that AI can address.
Collaborate with security and compliance to ensure AI features meet regulatory requirements for financial data handling, auditability, and data privacy.
Required Qualifications
8+ years of professional software engineering experience, with significant time spent building production systems at scale.
3+ years of hands-on experience building AI/ML-powered product features, including at least 1+ year shipping LLM-powered or agentic features into production (not research prototypes, but shipped software that real users depend on).
Deep experience with LLM integration: prompt engineering, function/tool calling, RAG architecture, agent orchestration, and evaluation frameworks.
Strong software engineering fundamentals: system design, API design, data modeling, distributed systems, and production operations.
Strong proficiency in Python. Working knowledge of TypeScript is a plus.
Track record of leading technical initiatives that span multiple teams or systems, with strong written communication (RFCs, design docs, ADRs).