Full stack engineer developing and scaling generative AI applications at PwC. Collaborating across teams to enhance software solutions and mentor junior engineers while maintaining high standards.
About the role
- Applied AI Engineer optimizing large language models for real-world healthcare workflows. Collaborating with engineers and product managers to develop robust solutions in healthcare systems.
Responsibilities
- 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
- Competitive compensation + meaningful equity + healthcare benefits
Job title
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Degree requirement
No Education Requirement
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