AI Data Engineer
About the Role
We are looking for skilled and hands-on Agentic / Generative AI Engineers (4–6 years experience) to design, build, and deploy LLM-powered applications and agentic workflows for enterprise use cases such as document intelligence, knowledge assistants, summarisation, and workflow automation. This role is ideal for professionals transitioning from Data Engineering / Data Science backgrounds who have built or contributed to production-ready GenAI solutions. You will collaborate with cross-functional teams to deliver scalable, reliable, and business-driven AI solutions. Mandatory: Prior experience in Data Engineering or Data Science with strong understanding of data pipelines or ML workflows
Key Skills for This Role
Full Job Posting
Key Responsibilities
- Design and develop LLM-based applications using single-agent or simple multi-agent patterns for business use cases
- Build and maintain RAG pipelines: data ingestion → chunking → embeddings → retrieval → response generation
- Implement prompt engineering techniques (prompt templates, chaining, basic tool/function calling)
- Develop backend services/APIs for AI applications using Python frameworks (FastAPI / Flask / Streamlit)
- Integrate AI solutions with enterprise systems, databases, and APIs
- Apply basic guardrails and validation checks to improve response quality and reduce hallucination
- Work with Data Engineering teams to ensure data quality, pipeline efficiency, and proper documentation
- Collaborate with MLOps teams for deployment, monitoring, and iterative improvements
- Document solutions, reusable components, and best practices
- Must-Have Skills
Experience
- 4–6 years total experience, with 1+ year hands-on experience in GenAI / LLM-based applications
- LLM / GenAI & Agentic Engineering
- 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
- Core Engineering
- 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
- Data / AI Foundations (Mandatory)
- Prior experience in one or more:
- Data Engineering (ETL/ELT, pipelines, orchestration)
- Data Science / ML lifecycle (especially NLP)
- Analytics engineering / data products
- Good-to-Have / Preferred
- Exposure to model fine-tuning (LoRA/PEFT) or prompt optimisation techniques
- Experience with evaluation of LLM outputs (quality, relevance, latency)
- Understanding of enterprise data privacy and security considerations in GenAI
- Exposure to Azure AI / Azure OpenAI / AI Search ecosystems
- Experience working on real client-facing AI solutions or POCs
Preferred Qualifications
- Good-to-Have / Preferred
About EXL
EXL is a global data and AI company that provides analytics, digital operations, and industry-specific services to enterprises in insurance, healthcare, banking, retail, media, and energy.
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