AI / LLM Engineer

Job Description:

• As an AI LLM Engineer, you will develop AI-enabled capabilities that power summaries, narrative explanations, recommendations, retrieval, evaluation, and governed human review experiences across an enterprise platform.
• You will partner closely with architects, product teams, data engineers, backend engineers, security, QA, and domain specialists to transform AI patterns into production-ready software components.

Key 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 (RAG) pipelines, including document ingestion, chunking, embedding generation, metadata tagging, vector indexing, retrieval tuning, and grounded response generation.
• Create 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 other equivalent approved platforms.
• Implement AI run logging, including prompt and model metadata capture, evidence and citations, output traceability, reviewer feedback capture, and human-in-the-loop controls.
• Develop AI evaluation routines covering 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 resolve production issues.
• Support responsible AI practices including prompt injection checks, data leakage prevention, policy-based guardrails, and AI output validation.

Technical Requirements:

• Minimum 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 standalone proof-of-concept demos.

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, including prompt model registry, AI audit logs, explainability, groundedness checks, and human review workflows.

Preferred Skills:

Technology->AI-Agentic AI->Agent Engineering,Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml,Technology->AI-Data science->AI-Data science,Technology->AI-Machine Learning->ML Platform,Technology->AI-Machine Learning->Responsible AI