Design, iterate, and optimize prompts that drive LLM behavior in production settings—spanning classification, summarization, structured extraction, and workflow automation.
Collaborate with engineers, product managers, and customers to translate domain problems (e.g. prior auth, claim appeals, denials) into robust LLM-powered solutions.
Build and maintain internal tooling for prompt versioning, testing, and performance evaluation (accuracy, latency, cost).
Fine-tune and evaluate models where needed using structured feedback and real-world data.
Analyze edge cases and failure modes; design prompt strategies to improve resilience and alignment with business logic.
Stay current on the LLM ecosystem and incorporate best practices into our architecture.

Requirements

2+ year of experience in a software engineering and LLM-centric role.
Deep understanding of prompt design patterns and LLM capabilities/limitations across GPT-4, Claude, Mistral, etc.
Hands-on experience with LangChain, LlamaIndex, PromptLayer, or similar frameworks.
Strong Python skills and comfort working in a codebase (bonus: experience with backend workflows or vector DBs like Weaviate/Pinecone).
Obsession with quality—you’re comfortable writing tests, measuring performance, and debugging model behavior with structured rigor.
Bonus: Exposure to healthcare data, terminology (EHRs, RCM, FHIR), or compliance-aware deployments.

Benefits

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

prompt designLLM capabilitiesPythonbackend workflowsvector databasesperformance evaluationmodel fine-tuningtestingdebuggingstructured feedback

collaborationproblem-solvingattention to detailquality obsession