Principal AI Engineer – Context – Agents and Context

Overview

In this role you own the end-to-end improvement loop for the Context Engine, shaping how AI agents, automations and skills behave in production. You will collaborate with data scientists, backend engineers, product and UX to drive observable, evaluable improvements that influence customer-facing outcomes. You’ll design telemetry and evaluation methods to inform product decisions and ship changes safely in an open codebase. The role combines engineering, data science, and product to scale intelligent enterprise data use.

Pay / Benefits

• competitive pay
• health coverage for you and family
• flexible locations and schedules
• generous vacation days
• donation matching up to 2000
• volunteer hours yearly

Responsibilities

• Own the production improvement loop for Context Engine, identifying and fixing failure modes from telemetry and customer feedback
• Define safe iteration strategies for agents and skills, including versioning, rollout, regression coverage, and guardrails
• Design telemetry to support data-informed engineering decisions across agent traces, tool calls, and knowledge retrieval
• Collaborate with the data science team on evaluation strategy, including golden datasets and latency/cost gates
• Review designs and PRs, mentor engineers in eval-driven development, and contribute to roadmap proposals

Key requirements

• 10+ years of software engineering with AI-driven production products
• Experience diagnosing agent/LLM behavior from traces and feedback, and shipping fixes with measurable outcomes
• Hands-on experience building agents with state and memory, and safe prompt/skill/tool iteration in production
• Familiarity with MCP and exposing public MCP servers and tools
• Experience designing telemetry for AI systems and using it to guide decisions
• Experience running end-to-end product experiments: instrumentation and result interpretation
• Experience building products with public APIs and evolving data models with external contracts
• Strong backend skills in Python or TypeScript for APIs, stateful workflows, data pipelines and production services
• Ability to collaborate with data scientists, engineers, and product managers and communicate trade-offs clearly
• Pragmatic, low-ego, distributed async-first work style

• collaboration
• clear communication of trade-offs
• mentorship
• TypeScript
• Python
• APIs

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