Agentic/AI engineers with Claude/code/LLM skills1

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Skills you'll use

What you'll do

• Design and build agentic LLM solutions (single- and multi-agent patterns) to solve real business problems across domains (e.g., customer support, document intelligence, knowledge retrieval).
• Build RAG pipelines end-to-end: data ingestion → chunking/embeddings → vector search → retrieval orchestration → response synthesis, with measurable quality.
• Implement prompt engineering and prompt orchestration (prompt chains, tool-calling, function calling), including prompt iteration and cost/latency optimisation.
• Develop production services/APIs for LLM applications (e.g., FastAPI/Flask/Streamlit) and integrate with enterprise systems and data sources.
• Apply guardrails to reduce hallucinations, enforce policy constraints, and ensure safe tool usage; implement evaluation strategies for LLM and RAG outputs.
• Collaborate with Data Engineering teams to ensure data quality, governance, and documentation standards, and with MLOps/Platform teams for CI/CD, monitoring, and reliable deployments.
• Create and maintain technical documentation, solution design artefacts, and reusable components for faster delivery and consistent engineering practices.
• Must-Have Skills
• 5 to 12 years total experience, with hands-on LLM/GenAI delivery experience (preferably 1–3+ years building production-grade LLM apps).
• LLM / GenAI & Agentic Engineering

Who can apply

• 5 to 12 years total experience, with hands-on LLM/GenAI delivery experience (preferably 1–3+ years building production-grade LLM apps).
• LLM / GenAI & Agentic Engineering
• Hands-on experience with LLMs including Claude (Anthropic) and other leading models; strong understanding of capabilities, limitations, and use-case fit.
• Practical experience with RAG, embeddings, vector databases (e.g., FAISS/Pinecone/ChromaDB), semantic search, and retrieval quality evaluation.
• Experience with frameworks/tools such as LangChain, LangGraph, Hugging Face, or equivalent orchestration stacks.
• Experience building agentic workflows including tool calling/function calling; familiarity with “agentic architecture” concepts is valued.
• Exposure to Claude Code or similar coding-agent workflows is a plus (agentic coding that can work across codebases, run tests, and iterate).
• Core Engineering
• Strong Python engineering skills (production-grade coding, testing, packaging, API development).
• Solid understanding of cloud platforms (Azure/AWS/GCP) and deployment basics (containers, CI/CD, monitoring).

About EXL

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