AI Data Engineer

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Skills you'll use

What you'll do

• 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

Who can apply

• 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:

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