Lead Engineer AI/Gen AI

About the Role

We re seeking an AI Software Development Lead to spearhead AI-assisted software development adoption across BFSI projects and lead solutioning for client proposals and pre-sales engagements.

Will champion vibe coding the emerging practice of using LLMs and coding agents (e.g., GitHub Copilot, Cursor, Claude Code, etc.) to generate working code from natural-language instructions, iterating rapidly while enforcing quality and compliance. Your leadership will modernize engineering workflows and scale AI-first development practices across diverse BFSI portfolios.

Will architect and deliver enterprise-grade AI applications leveraging Generative AI (GenAI), Agentic AI, LLMs, RAG, and Agentic RAG with a strong focus on security, governance, observability, and cost efficiency.

This role operationalizes AI-first delivery, increases developer productivity, strengthens proposal win rates through compelling AI solutioning, and ensures secure, compliant implementations aligned with BFSI standards.

Key Responsibilities

1. AI-Assisted Development Leadership

a. Drive organization-wide adoption of coding agents and vibe coding practices; define guardrails, standards, and governance for BFSI environments.

b. Build playbooks for prompt engineering, code generation, refactoring, test generation, documentation, and secure patterns using Copilot/Cursor/Claude Code, etc.

c. Deliver enablement programs: workshops, hands-on labs, brown-bags; establish usage analytics and productivity KPIs.

2. Solutioning, Pre-Sales & Proposal Support

a. Partner with sales, pre-sales, service lines, and delivery to:

tailor AI-first roadmaps, demo assets, and POCs/Pilots

lead technical solutioning for RFPs/RFIs: author architecture options, reference designs, delivery models, and cost estimates.

Create client-facing proposals with clear business outcomes, risk/compliance alignment, and measurable success metrics.

3. Architecture & Delivery (LLMs, RAG, Agents)

a. Architect and deliver agentic systems tool orchestration, planning/critique loops, memory, multi-agent collaboration for complex BFSI workflows.

b. Own end-to-end solutioning: data acquisition/transform; embeddings/retrieval; prompt pipelines; function calling/tool schemas; APIs/SDKs; UI integration.

4. RAG & Agentic RAG Best Practices

a. Design advanced RAG pipelines: chunking, hybrid retrieval (vector + keyword), rerankers, query rewriting, context compression, caching, grounding, and citations.

b. Build Agentic RAG flows combining retrieval + tool use + planning loops to maximize accuracy, policy adherence, and cost performance.

5. Quality, Evals & Observability

a. Define LLM/agent evaluation: groundedness, factuality, precision/recall, hallucination rate, agent success rate, latency, cost/query.

b. Implement observability: tracing, token/cost accounting, prompt/version lineage, user feedback loops, and red-team logs.

6. Collaboration & Leadership

a. Mentor engineers; lead design reviews and AI SDLC standards; influence architecture councils.

b. Drive build-vs-buy decisions, vendor evaluations, and cost/latency optimization strategies.

Required Qualifications

10-12+ years in software engineering, including enterprise architecture and delivery; 3+ years hands-on with LLMs/GenAI.

Proven BFSI exposure (banking, payments, insurance, capital markets, fintech) with security/compliance constraints.

Strong software engineering fundamentals and coding in Python plus one of Java/TypeScript/C#/Scala; production APIs/microservices; CI/CD.

Hands-on with coding agents & IDEs: GitHub Copilot, Cursor, Claude Code etc. and IDE integrations (VS Code/IntelliJ/JetBrains); expert in vibe coding workflows.

Deep knowledge of LLM ecosystems (e.g., Azure OpenAI/OpenAI, Anthropic, Google, Meta): prompting, function calling, MCP (Model Context Protocol), token/cost management.

Expertise in RAG: vector DBs (FAISS, Pinecone, Milvus, pgvector/Postgres, Elastic/OpenSearch), embedding strategies, and rerankers.

Experience with agent frameworks: LangGraph, AutoGen, Sematic Kernel, CrewAI (or similar) and tool integrations.

LLMOps and eval tooling: LangSmith, TruLens, Ragas, DeepEval, W&B, MLflow; prompt caching/compression; distillation.

Infra & data: Docker/Kubernetes, Azure/AWS/GCP, Kafka, Airflow; API security and secrets management.

Testing & quality: unit/integration/e2e, canary/blue-green, A/B or interleaving experiments for AI features.

Excellent communication; able to translate BFSI needs into reliable AI systems with clear KPIs.

Education & Certifications

Bachelor s/Master s in Computer Science, Software Engineering, Data/AI, or related field (or equivalent experience).

Preferred: Cloud architect/ML/AI certifications (Azure/AWS/GCP)