Job Title: AI Tech Lead / AI Architect

Location: India (Hybrid) Chennai, Bangalore, Hyderabad, Mumbai, Pune, Delhi, Puducherry, Trichy

Domain: Data, Analytics & AI

Employment Type: Full-time

Role Level: Hands-on GenAI Delivery Leadership

Role Summary:

We are seeking a hands-on AI Tech Lead / AI Architect to own the technical direction and day-to-day delivery of Generative AI engineering pods serving enterprise financial-services clients. Sitting between Senior AI Engineers and Engagement Managers, you will architect solution patterns, review code, unblock engineering teams, and serve as the primary technical voice with client architects, platform, and security teams. You will turn complex business requirements into scalable technical designs, detailed implementation plans, and precise estimates - taking full accountability for bringing production-ready AI solutions to life.

Key Responsibilities:

Technical Leadership & Architecture:

- Solution Architecture: Own end-to-end architecture for enterprise GenAI applications (RAG, agentic workflows, document intelligence, evaluation, and guardrails) primarily on Azure (Azure OpenAI / AI Foundry, AI Search, Cosmos DB, Functions / App Service, APIM / AI Gateway).

- Engineering Standards: Set and enforce robust standards across code repositories, including branching strategies, PR code reviews, automated testing, Infrastructure as Code (Terraform), CI/CD pipelines (GitHub Actions), observability, security, and cost optimization.

- Architecture Trade-offs: Make, document, and defend architectural calls regarding model selection and routing, retrieval strategies, latency vs. cost trade-offs, and build vs. buy decisions.

- Evaluation & Responsible AI Guardrails: Embed evaluation metrics from day one: golden datasets, regression testing gates on prompt/code updates, LLM-as-judge frameworks, content filters, and human-in-the-loop validation for high-risk financial outputs.

- Production Readiness & Delivery: Lead release readiness reviews, operational hand-offs, UAT support, release documentation, and rollback strategies.

People, Delivery & Client Engagement:

- Pod Leadership: Lead and mentor a multi-disciplinary pod of 4 - 8 engineers (AI, backend, data, full-stack).

- Conduct sprint planning, task allocations, code/design reviews, and serve as the first point of escalation.

- Delivery Ownership: Estimate, plan, and track technical deliverables. Proactively identify and resolve technical dependencies (access, client environments, security approvals).

- Client Interface: Act as the senior technical point of contact with client architects, security, platform, and governance teams; lead design walkthroughs, demos, and decision meetings.

- Scoping & Communication: Translate unstructured business asks into scoped, phased technical plans; push back constructively with alternative options when constraints arise.

Required Qualifications & Experience:

- Total Experience: 8 - 12 years in software, data, or ML engineering with progressive technical leadership experience.

- GenAI Experience: 3+ years of hands-on experience architecting and shipping Generative AI applications to production environments.

- Engineering Leadership: 2+ years leading engineering teams, managing task allocations, conducting code reviews, and driving team growth.

- GenAI & LLM Mastery: In-depth experience with LLM design patterns (Advanced RAG, hybrid search & re-ranking, agentic systems/MCP, structured outputs) and observability tools (RAGAS, LangSmith, Langfuse, or similar).

- Azure AI Ecosystem: Production experience with Azure OpenAI / AI Foundry, AI Search, Cosmos DB, Functions / App Service, Key Vault, private networking, and Entra ID / managed identities. (Strong AWS background with a clear transition path to Azure is acceptable).

- Backend & DevOps: Advanced Python skills (FastAPI, AsyncIO), Docker, Terraform, CI/CD automation, and multi-environment promotion strategies.

- Responsible AI & Security: Solid grounding in prompt-injection mitigation, PII mask/guardrails, content filtering, audit logging, and compliance practices in regulated financial environments.

Preferred Experience:

- Domain experience in Financial Services, Banking, or Asset Management. Hands-on experience with AI gateways, API management (APIM, Kong), dynamic model routing, OCR/Document Intelligence (Azure Document Intelligence / Content Understanding). Practical familiarity with React for chat interfaces and operational UIs.