Applied AI Engineer

Future is building a personalized guidance system for lifelong health. They are seeking an Applied AI Engineer to help build and ship AI-powered features that enhance product experience and business outcomes.

Responsibilities:

• 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

Requirements:

• 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 calling, structured output, evaluation
• Comfort with async Python, HTTP APIs, and streaming protocols (SSE, webhooks)
• Experience with data validation and schema design (Pydantic, JSON Schema)
• Ability to debug across layers: from a broken LLM tool call to a misconfigured Terraform resource
• Clear communication: you'll work directly with product, mobile, and backend engineers
• Familiarity with AWS (Bedrock, ECR, CloudFront, S3, Cognito) or other cloud agent hosting
• Observability and tracing tools (Langfuse, OpenTelemetry, Datadog)
• Exposure to evaluation frameworks: LLM-as-a-judge, automated scoring, dataset management
• Infrastructure-as-code (Terraform, CDK)