What You'll Do

Design and ship LLM-powered product features end to end, including prompt and context design, structured output, tool/agent orchestration, and the UI/API work needed to land them in the product.
Pair with product-vertical engineers to unblock AI feature work and help teams apply August Intelligence patterns in their own product areas.
Turn repeated AI implementation work into reusable tools, examples, documentation, and lightweight practices that other teams can adopt.
Build and extend our evaluation discipline so we can measure quality, catch regressions, and reason about nondeterministic behavior before customers feel it.
Improve observability for AI workflows, including traces, prompt visibility, errors, latency, cost, and production feedback loops.
Harden AI features for production: latency, cost, caching, pre-computation, timeouts, cancellation, memory behavior, and operational reliability.
Handle PHI and sensitive resident data with care, including redaction, permissioning, tenancy boundaries, and safe access patterns.
Partner with product managers and vertical engineering teams to translate clinical and operational workflows into AI capabilities.
Help August learn where AI can make the product meaningfully more useful by prototyping, measuring, and refining ideas against real workflows.
Help non-engineers experiment safely with real data while creating a clear path from prototype to production.
Help drive responsible AI tool use inside engineering by modeling effective workflows, reviewing AI-assisted output, and raising shared standards without turning experimentation into ceremony.
Review code, including AI-assisted code, with rigor and help set August Intelligence's engineering standards.

About You

5+ years of experience building and shipping production software.
Owned customer-facing features end to end and care about what happens after launch.
Strong in production TypeScript, including typed Node services, testing, APIs, and clean data modeling.
Hands-on applied-LLM experience: prompt design, structured output, tool or agent design, and practical reasoning about cost, latency, hallucination, and reliability tradeoffs.
Bias toward the smallest useful thing: ship a walking skeleton, learn from it, and iterate rather than guessing future states and going big too early.
Can explain exactly how your code works, including code an LLM helped you write.
Enjoy pairing with other engineers, teaching through real work, and helping teams adopt new patterns without taking ownership away from them.
Comfortable working with sensitive data and treat permissioning, privacy, and safety as table stakes.
Communicate clearly in writing and conversation, especially when working with PMs, clinicians, operators, and engineers from other product areas.
High agency and low ego. Like shaping ambiguous problems, not just implementing tickets.

Nice to Have

Experience with Mastra, the Vercel AI SDK, LangChain, LlamaIndex, or similar applied-LLM frameworks.
Experience with eval or observability tooling for LLM systems, such as Phoenix/Arize, Langfuse, Helicone, or related tools.
Experience helping an engineering team adopt AI tools, agentic coding workflows, or new development practices responsibly.
Experience with functional TypeScript or Effect.
Ability to read Scala or interest in learning enough Scala to navigate our backend.
Healthcare, senior living, SOC 2, HIPAA, or other regulated-industry experience.
Experience as an early engineer on a small, high-ownership team.