Job Description:
- As an AI LLM Engineer you will build the AI enabled capabilities that support summaries narratives explanations recommendations retrieval evaluation and governed human review experiences across an enterprise platform
- You will work closely with architects product teams data engineers backend engineers security QA and domain specialists to convert 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 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
Technical 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
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
Preferred Skills:
Technology->AI-AI Engineering->AI/ML Solution Architecture and Design->traditional ai ml,Technology->AI-AI Engineering->LLMOps,Technology->AI-Data science->Amazon ML,Technology->AI-Data science->PYTHON,Technology->AI-Generative AI->Generative AI - Basic->retrieval augmented generation (rag),Technology->Cloud Platform->Azure Networking Services->Azure Bastion,Technology->Enterprise Architecture->API / Microservices Architecture,Technology->Enterprise Architecture->Digital Architecture