AI / LLM Engineer

Responsibilities

• 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.

Additional Responsibilities

• 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.

Technical and Professional Requirements

• Minimum 7–10 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.

Preferred Skills

• traditional ai ml
• LLMOps
• Amazon ML
• PYTHON
• retrieval augmented generation (rag)
• Azure Bastion
• API / Microservices Architecture
• Digital Architecture

Educational Requirements

Master Of Engineering,Bachelor of Engineering