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Tech Stack
Tools & technologiesAWSAzureCloudETLFlaskGoogle Cloud PlatformPySparkPythonSQL
About the role
Key responsibilities & impact- 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
- 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
- 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
- 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)
- Implement LLM guardrails: Hallucination control, Safety filters, Policy enforcement
- Define evaluation frameworks: Response quality metrics, RAG benchmarking, Human-in-the-loop validation
- 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
- 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.
Requirements
What you’ll need- 9–12 years total experience
- 2–4+ years hands-on in LLM / GenAI delivery (production use cases)
- Strong hands-on experience with LLMs (Claude, OpenAI, etc.)
- RAG pipelines and retrieval optimisation experience
- GPT + Agentic AI implementation experience
- Experience with LangChain, LangGraph, or similar frameworks
- Deep understanding of LLM limitations, evaluation, and optimisation strategies
- Strong Python/Pyspark engineering expertise
- Deep data analysis experience and handling large volume of data
- Fabric/Azure Databricks/Snowflake data engineering integration skills
- Good exposure to cloud platforms (Azure/AWS/GCP), SQL, Containers, CI/CD, monitoring
- Prior experience in Data Engineering (ETL/ELT, pipelines, orchestration) or Data Science / ML lifecycle (especially NLP) or Analytics engineering / data products
- Experience leading solution design or small teams
- Ability to translate business problems into AI solutions
- Strong stakeholder communication and influencing skills
Benefits
Comp & perks- Health insurance
- 401(k) matching
- Flexible work hours
- Paid time off
- Professional development opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
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Hard Skills & Tools
LLMGenAIRAG pipelinesprompt engineeringFastAPIFlaskPythonPysparkdata analysisdata engineering
Soft Skills
mentoringstakeholder communicationinfluencingend-to-end delivery ownershiptechnical reviewssolution walkthroughscode reviewsdesign governanceteam leadershipbusiness problem translation