Applied AI engineer
Q1 Technologies
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PTN_US_GBAMSREQID_CandidateBeelineID i.e. PTN_US_9999999_SKIPJOHNSON0413 MSP Owner: William Bristol Location: See Below Duration: 6 months Must Have Technical/Functional Skills Claude Code and AI-assisted development • Strong day-to-day experience with Claude Code, including a disciplined workflow: plan before coding, use CLAUDE.md project memory, implement in small steps with tests, and review the code it generates. • Has run parallel workstreams using git worktrees, multiple sessions, subagents with focused roles (such as tester, reviewer or refactorer), custom slash commands, hooks and MCP integrations. • Can deliver deployable progress on several projects in the same day without sitting idle while a model generates. Applied AI engineering • Has built and taken to production RAG systems, with hands-on use of parsing and chunking libraries (for example Unstructured, Docling, LangChain or LlamaIndex splitters). • Solid grasp of hybrid search (BM25 plus dense vectors, reciprocal rank fusion) and cross-encoder reranking. • Has built agentic and multi-agent systems with LangGraph or a similar framework (CrewAI, AutoGen, Semantic Kernel), and understands state, checkpointing, conditional routing and interrupts. • Has built LLM evaluation using golden datasets, retrieval metrics (View email address on click.appcast.io, MRR), faithfulness and groundedness checks, and LLM-as-judge, with tools such as Ragas, DeepEval or Promptfoo. • Can optimize cost and latency through model routing, prompt caching, context reduction and batching. • Strong Python and FastAPI, plus working knowledge of Docker and CI/CD. Open-source stack and best practices • Embeddings: open-source models such as bge, e5 or nomic, with the ability to benchmark them against commercial options. • Knows how to avoid vendor lock-in, check licenses on models and libraries, pin versions, and keep components modular so they can be swapped. Cloud • Preferred: Azure and GCP. For example, Azure OpenAI, Azure AI Search, Azure AI Document Intelligence, Vertex AI, Vertex AI Vector Search, GKE or AKS. Nice to have • Healthcare domain experience (payer or provider), with knowledge of PHI and HIPAA requirements and familiarity with FHIR. • Guardrails and security for LLM apps: PHI masking, prompt-injection defense, grounded answers with citations, audit logging. • Fine-tuning experience (LoRA or QLoRA) on open-weight models. Roles & Responsibilities We're looking for a hands-on Applied AI Engineer who builds and ships AI products. You'll join a team that finds AI opportunities across the business and turns them into working POCs and production applications, including chatbots, document intelligence, and agent-driven workflows. You'll often run 3-4 projects at once and deliver real progress on each one every day. To do that, you'll need to use AI coding tools well. • Design, build and productionize RAG, agentic AI and multi-agent orchestrated applications, from POC through deployment and support. • Own the full pipeline: ingestion and parsing, chunking, embeddings, indexing, hybrid retrieval, reranking, generation, evaluation and monitoring. • Build multi-agent workflows with state management, tool calling, retries and human-in-the- loop checkpoints.- Put evaluation frameworks and guardrails in place for accuracy, faithfulness, safety and cost.
- Use Claude Code daily to move several independent projects forward in parallel.
Vacancy posted 3 days ago
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