AI Engineers

Pangaea is looking for a skilled AI Engineer to build and productise LLM and agent capabilities in Pangaea’s AI Platform. This is an applied product engineering role rather than primarily a model-training or research position.

You will own capabilities from problem definition through implementation, evaluation, release and monitoring. Typical work includes turning patient notes, FHIR data and clinical guidelines into evidence-grounded structured outputs; building retrieval, reasoning, and tool-using agents; and combining probabilistic models with deterministic clinical logic. You will work closely with clinicians, who remain the authority on clinical interpretation and approval., * Collaborate with clinicians and product stakeholders to translate clinical problems into explicit data contracts, system behaviour and success measures.

• Design and ship LLM and agent workflows for clinical evidence extraction, retrieval, reasoning, patient identification, guideline-driven review and conversational experiences.
• Build robust LLM integrations using structured outputs, schema validation, tool calling, bounded context and explicit workflow state.
• Develop retrieval and data pipelines across structured clinical data and free text, preserving provenance and links to supporting evidence.
• Create evaluation datasets and regression checks for prompt, model and provider changes, combining automated assessment with clinician review where appropriate.
• Instrument traces, failures, latency, token use and cost, and implement suitable timeout, retry, rate-limit, concurrency and caching behaviour.
• Productise capabilities as maintainable Python services and APIs, delivering small improvements regularly and monitoring their impact after release.
• Integrate AI capabilities with data, backend and frontend systems and contribute to architecture, code review and documentation.
• Gather early feedback from clinicians and internal users and use production telemetry to improve quality and usability.
• Communicate technical trade-offs, limitations, roadmap decisions and product changes clearly before launch.

While expertise across all areas is not required, ideal candidates will possess a solid foundation in production LLMs, complemented by deep specialization in either agent and backend architectures or clinical data analytics and model evaluation.

Technical Skills

• Demonstrated experience shipping LLM-enabled software used by real users or operational teams.
• Strong Python and software engineering skills, including typed data models, APIs, automated testing and maintainable system design.
• Hands-on experience with hosted LLM APIs, structured outputs, tool use, retrieval-augmented generation, context management and model limitations.
• Experience evaluating non-deterministic systems using representative datasets, explicit metrics, failure analysis and regression testing.
• Experience with databases, search or data-processing systems and diagnosing behaviour across service boundaries.
• A rigorous approach to evidence, provenance, ambiguity and safe failure in a high-stakes domain.
• A degree in computer science, engineering, data science or a related subject, or equivalent practical experience., * A strong intuition for what makes products a joy to use
• Empathy for how different users will need different things out of a product at different stages, and how to effectively serve these different needs in one product
• Strong communication and mediation skills
• Strong people skills and the ability to engage all levels of the organization (especially the front line).
• Ability to work collaboratively in a team environment.
• Ability to communicate complex ideas effectively, both verbally and in writing
• A strong software engineering background with machine learning expertise to understand how the user facing product will tie into backend and architectural decisions, * Experience with FHIR R4, HL7, LOINC, SNOMED CT or longitudinal clinical data.
• Experience in healthcare, life sciences or another regulated or safety-sensitive domain.
• Familiarity with FastAPI, Pydantic, MongoDB, Elasticsearch, hybrid or vector search, LangGraph or similar agent frameworks.
• Experience with Azure or AWS, containers, on-premise deployments, multi-tenant systems, privacy controls or audit logging.
• Experience with embeddings, fine-tuning, open-source models or applied research when these are the right tools for a product problem.

• Benefits include private medical insurance, life insurance and travel cards.
• You would join a small, dedicated and fast-growing team.
• You will have the opportunity to learn about building a startup business from experienced professionals and serial entrepreneurs.
• Pangaea is currently supported by serial entrepreneurs and angel investors. You will have the opportunity to experience an investment life cycle for a startup and meet leading venture capitalists.

Pangaea Data (Pangaea) is a provider of a clinically validated AI platform that proactively uncovers care gaps, which cannot be pre-empted or prompted for because they are unknown, thereby delivering reliable, actionable insights that enable earlier intervention, improve quality and patient safety, and make advanced clinical decision support accessible across both high and low resource settings. Pangaea’s founders Dr Vibhor Gupta and Prof Yike Guo (Director Data Science Institute at Imperial College London; Provost, Hong Kong University of Science and Technology)have worked in medicine and computing for over 20 years and have raised over $300 million through their academic research, including a $110 million grant focused on development work on large language models in medicine. Their advisors include industry veterans from healthcare and the life sciences, including Lord David Prior (former chairman, NHS England) and Mr. Andy Palmer (former CIO, Novartis).