Applied AI Engineer

Excerpt from the original posting

About Us: 

Future is building a personalized guidance system for lifelong health. We help people understand what to do next for their body, goals, and stage of life — then support them in turning those decisions into sustained behavior change. By combining AI, human expertise, personal health data, and accountability, Future helps members improve performance today while building the resilience, capacity, and healthspan they need for decades to come.

About the Role 

We're looking for an Applied AI Engineer to help us build and ship AI-powered features that directly improve our product experience and business outcomes. This is a hands-on, product-focused role where you'll take ideas from concept to production — designing intelligent systems, validating them with real users, and turning them into reliable, scalable services.

You'll work at the intersection of AI, product, and engineering — partnering closely with cross-functional teams to identify high-impact opportunities, prototype quickly, and iterate based on data. This isn't a research-only role. You'll own the full lifecycle: experimentation, evaluation, deployment, monitoring, and continuous improvement.

The ideal candidate is excited about applying LLMs and modern ML tooling to real-world problems. You think in terms of systems, tradeoffs, and outcomes — not just models. You care about performance, quality, latency, and cost in production. Most importantly, you're motivated by shipping impactful AI experiences that customers actually use.

What You'll Do 

- Build and ship AI agents that serve real users: tool-calling LLM systems with structured output, parallel API orchestration, and streaming responses.

- Design evaluation harnesses and quality scoring — we use Langfuse, rubrics to measure safety, effectiveness, and personalization.

- Own the full loop: prototype a new agent capability, validate it with evals, deploy it to staging and production, monitor traces, and iterate.

- Improve reliability, latency, and cost through prompt caching strategies, token budgets, retry logic, and observability.

- Write the tools agents use: API integrations with Pydantic validation, exercise search over local databases, structured workout submission.

What You Bring 

- Strong Python skills: you've built and deployed services on large production systems.

- Experience with LangChain/LangGraph or similar agent frameworks.

- Hands-on experience with LLMs in production: prompt engineering, tool/function call…

→ Jobicy