Key skills
AWSAzureCloudETLFlaskGoogle Cloud PlatformPySparkPythonSQLAIMLNLPGenAILLMOpenAIClaudeRAGLangChainAgenticLangGraphELTData EngineeringAnalyticsSnowflakeDatabricksFastAPIGCPGoogle CloudCI/CDCommunicationSales
About this role
Role Overview
- 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, and 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 and evaluate different architectures
- Develop production-grade APIs/services (FastAPI, Flask, etc.) and drive code quality, testing standards, and reusable architecture components
- Implement LLM guardrails, define evaluation frameworks, and drive end-to-end delivery ownership across multiple projects
- Mentor and guide junior engineers and project teams, supporting pre-sales/RFPs/solution proposals with architecture inputs
- Stay ahead of industry evolution and help shape EXL’s GenAI strategy
Requirements
- 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
- Deep understanding of LLM limitations, evaluation, and optimisation strategies
- Strong Python/Pyspark engineering expertise 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
- Prior experience in Data Engineering (ETL/ELT, pipelines, orchestration), Data Science / ML lifecycle (especially NLP), 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
Tech Stack
- AWS
- Azure
- Cloud
- ETL
- Flask
- Google Cloud Platform
- PySpark
- Python
- SQL
Benefits
- 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, and 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 and evaluate different architectures
- Develop production-grade APIs/services (FastAPI, Flask, etc.) and drive code quality, testing standards, and reusable architecture components
- Implement LLM guardrails, define evaluation frameworks, and drive end-to-end delivery ownership across multiple projects
- Mentor and guide junior engineers and project teams, supporting pre-sales/RFPs/solution proposals with architecture inputs
- Stay ahead of industry evolution and help shape EXL’s GenAI strategy