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Tech Stack
Tools & technologiesAWSAzureCloudFlaskGoogle 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)
- 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.
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
- GPT + Agentic AI implementation experience
- Experience with LangChain, LangGraph, or similar frameworks
- Agent orchestration and tool-calling architectures
- Deep understanding of LLM limitations, evaluation, and optimisation strategies
- Strong Python/Pyspark engineering expertise (production-grade development) with proven API integration experience
- 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
- 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
- Retirement plans
- Paid time off
- Flexible work arrangements
- Professional development opportunities
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
LLMGenAIRAG PipelinesPrompt EngineeringAPI DevelopmentData AnalysisSQLPythonPysparkAgent Orchestration
Soft Skills
Stakeholder CommunicationTeam LeadershipMentoring