Role Overview

We are hiring Lead Agentic / Generative AI Engineers (912 years) to architect and deliver enterprise-grade LLM-powered and agentic systems at scale.

This is a hands-on technical leadership role requiring:

- Deep expertise in LLMs, RAG, and agentic architectures
- Robust foundation in Data Engineering / Data Science
- Ability to lead solution design, mentor teams, and drive end-to-end delivery

You will partner with cross-functional teams (Data Engineering, Data Science, MLOps, Product) to build secure, scalable, and measurable GenAI solutions, while also owning technical direction and best practices.

Key Responsibilities

1. Solution Architecture & Technical Leadership

- Architect enterprise-grade agentic and LLM solutions (single-agent, multi-agent, tool-driven workflows)
- Define scalable GenAI system design patterns (RAG, orchestration layers, evaluation frameworks)
- Act as the technical anchor for GenAI initiatives across projects
- Drive design reviews, architecture governance, and best practices

2. Agentic AI & LLM Engineering

- Design and build agentic systems using LLMs for use cases such as:
- Knowledge assistants
- Document automation & intelligence
- Workflow orchestration

- Implement advanced prompt engineering strategies, prompt orchestration, and reasoning chains
- Build tool-calling / function-calling frameworks for agent workflows

3. RAG & Retrieval Systems

- Lead end-to-end implementation of RAG pipelines:




- Data ingestion chunking embeddings vector indexing retrieval response generation

- Optimise retrieval quality (recall, relevance, grounding)
- Evaluate and benchmark different architectures

4. Productisation & Engineering Excellence

- Develop production-grade APIs/services (FastAPI, Flask, etc.)
- Drive code quality, testing standards, and reusable architecture components
- Ensure solutions are performance optimised (latency, cost, reliability)

5. Governance, Safety & Evaluation

- Implement LLM guardrails:
- Hallucination control
- Safety filters
- Policy enforcement

- Define evaluation frameworks:

- Response quality metrics
- RAG benchmarking
- Human-in-the-loop validation

6. Collaboration & Delivery Leadership

- Partner with:
- Data Engineering pipelines, data quality, governance
- MLOps deployment, CI/CD, monitoring
- Business/Product use-case alignment

- Drive end-to-end delivery ownership across multiple projects

7. Technical Leadership Responsibilities (Critical Addition)

- Mentor and guide junior engineers and project teams
- Conduct technical reviews, solution walkthroughs, and code reviews
- Support pre-sales / RFPs / solution proposals with architecture inputs
- Drive reusable accelerators, frameworks, and COE assets
- Stay ahead of industry evolution and help shape EXL’s GenAI strategy
- Influence technology choice, design decisions, and roadmap planning

📌 Lead Data Engineer (Noida)
🏢 Quadrangle
📍 Noida