AI Engineer

Why TelepatiaTelepatia AI is a healthtech company on a mission to transform healthcare in Latin America and beyond.

Born at Stanford and built by doctors for doctors, our platform introduces AI Healthcare Employees that work invisibly inside clinical workflows — transcribing consultations in real time, generating structured medical records, offering evidence-based decision support, and integrating seamlessly with any current health system — all without disrupting the physician’s flow.

What You'll DoDesign, build, and scale Python microservices (FastAPI) that handle real-time inference and structured data processing — built to serve millions of users reliably.

Build and architect AI agent pipelines for reasoning, orchestration, and multi-step workflows — using the right tool for the job, whether that's an existing framework or a custom solution.

Own AI systems end-to-end: from initial design through production, including monitoring, cost management, failure modes, and observability.

Develop data pipelines and eval dashboards to monitor, analyze, and continuously improve AI model performance in production.

Integrate models and pipelines into our core Medical Scribe product to enhance automation, reliability, and the doctor experience.

Shape and execute on ambiguous, non-technical requests alongside clinicians, product managers, and software engineers — pushing back when needed, proposing better approaches, and shipping fast when the path is clear.

Set technical standards and raise the engineering bar for how AI systems are built and evaluated at Telepatia.

What You HaveSenior backend engineering experience at the AI application layer — LLM orchestration, agents, and pipelines.

Hands-on with AI frameworks (LangChain, LangGraph, PydanticAI, or similar) and production-ready integrations across model providers, embeddings, and third-party AI services.

Strong Python skills; FastAPI preferred.

Track record scaling AI systems to millions of users with real attention to latency, cost, and reliability.

Fluent in context engineering — RAG, vector stores, knowledge graphs, and grounding agents with large-scale data.

Comfortable with any database stack: Postgres, Firestore, graph DBs, vector stores.

Experience with data pipelines, model evaluation, and AI observability.

Sharp problem-solver who keeps things simple, practical, and maintainable.

Ownership mindset — you drive outcomes, not just tasks, in fast-moving, ambiguous environments.

Bonus: healthcare/EHR data, LGPD/HIPAA compliance, or early-stage startup experience.

Fluent in english