Senior applied ai engineer

Seeking a hands-on Applied AI Engineer with 8–10 years of experience, including 7+ years in AI engineering, to build and deploy production-grade RAG systems, agentic AI applications, chatbots, document intelligence, and multi-agent workflows. The role requires strong Python skills and daily use of Claude Code to deliver multiple projects in parallel.

Roles & Responsibilities

• Design, build, deploy, and support RAG, agentic AI, and multi-agent applications from POC to production.
• Manage the full RAG pipeline, including ingestion, parsing, chunking, embeddings, indexing, hybrid search, reranking, generation, evaluation, and monitoring.
• Use Claude Code with structured planning, CLAUDE.md, incremental implementation, testing, code reviews, Git worktrees, subagents, hooks, slash commands, and MCP integrations.
• Develop multi-agent workflows using LangGraph or similar frameworks, with state management, tool calling, retries, conditional routing, and human-in-the-loop checkpoints.
• Implement LLM evaluation, safety guardrails, retrieval metrics, groundedness checks, and cost and latency optimization.
• Build scalable APIs using Python and FastAPI, supported by Docker and CI/CD.
• Follow open-source best practices, benchmark embedding models, manage licenses and dependencies, and maintain modular, vendor-neutral architectures.
• Collaborate in Agile projects and deliver working solutions across multiple parallel workstreams.

Essential Skills

• Applied AI Engineering and Production AI/ML Applications
• Claude Code and AI-Assisted Development
• Python, FastAPI, and RAG
• LangGraph or Similar Agentic AI Frameworks
• Hybrid Search, Vector Embeddings, and Reranking
• LLM Evaluation, Guardrails, and Monitoring
• Docker, CI/CD, and API Development

Desirable Skills

• Cloud: Azure OpenAI, Azure AI Search, Azure AI Document Intelligence, Vertex AI, Vertex AI Vector Search, GKE, and AKS
• Open-Source Models: BGE, E5, Nomic, LoRA, and QLoRA
• Healthcare: Payer/provider experience, PHI, HIPAA, and FHIR
• AI Security: PHI masking, prompt-injection defense, grounded responses with citations, and audit logging
• Evaluation Tools: Ragas, DeepEval, and Promptfoo

Experience Required

8–10 years overall experience, including 7+ years in AI engineering. Proven experience building, deploying, and troubleshooting production AI systems. Hands-on coding capability; candidates should be able to demonstrate code they have written and systems they have shipped. Interview includes a live hands-on exercise using Claude Code.