01Overview
AI / LLM Engineer Minimum 710 years of experience in software engineering, AI/ML engineering, applied ML, data science engineering or related roles. Strong hands-on Python programming experience and practical exposure to LLM-based application development. Experience with RAG, vector databases, embeddings, prompt engineering, evaluation frameworks and AI service integration. Experience with frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, AutoGen, CrewAI or equivalent tools. Working knowledge of REST APIs, microservices, SQL, structured data concepts, Git workflows, testing and software engineering practices. Understanding of document extraction, semantic search, NLP, retrieval quality, hallucination risk, prompt safety and AI evaluation methods. Ability to build production-oriented AI components rather than isolated proof-of-concept demos. Experience with Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure Document Intelligence, Google AI Studio/Gemini, AWS Bedrock or Vertex AI. Exposure to RAG evaluation tools such as RAGAS, DeepEval, Promptfoo, LangSmith or equivalent frameworks. Experience with AI governance, prompt/model registry, AI audit logs, explainability, groundedness checks and human review workflows. Design and implement LLM-powered workflows for summarization, narrative generation, classification, extraction, contextual reasoning, explanation and reviewer-assist use cases. Build retrieval-augmented generation pipelines including document ingestion, chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning and grounded response generation. Develop reusable prompt templates, prompt versions, context builders, response schemas, evaluation routines and AI orchestration services. Integrate with enterprise AI services such as Azure OpenAI, Azure AI Foundry, OpenAI APIs, Google Gemini, Anthropic, Hugging Face or equivalent approved platforms. Implement AI run logging, prompt/model metadata capture, evidence citations, output traceability, reviewer feedback capture and human-in-the-loop controls. Build AI evaluation routines for answer quality, retrieval quality, hallucination checks, regression testing, consistency and groundedness. Collaborate with backend and DevOps teams to containerize AI services, deploy them securely, monitor usage, track costs and troubleshoot production issues. Support responsible AI practices such as prompt injection checks, data leakage prevention, policy-based guardrails and AI output validation.
