AI / LLM Engineer (10+)
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.
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