Sr. AI Platform Engineer

Own how our AI systems are deployed, operated, monitored, and scaled.

Build and maintain containerized deployments, CI/CD pipelines, infrastructure as code, environments, observability, alerting, and cost monitoring across AWS.

Create infrastructure that is reliable, secure, scalable, and cost effective.

Build the dashboards and monitoring systems that allow the team to understand system performance and identify issues before they become problems.

Build the durable state, client memory, and retrieval layers that support our AI agent applications.

Create systems that allow client profiles, commitments, history, and other important information to persist across interactions and over time.

Make those systems fast, reliable, observable, and scalable.

You won’t own the overall agent design. You’ll own the infrastructure that makes the agent work consistently in production.

Turn established evaluation criteria and benchmark data into automated systems that measure AI performance at scale.

Build automated evaluations that can run through CI and help determine whether new releases are ready for production.

Develop regression testing and monitoring processes that help the team identify performance issues and maintain quality as the system evolves.

Treat client and conversation data as sensitive information.

Build and maintain appropriate systems for retention, access control, encryption, and data security.

Develop a clean and reliable process for removing client information when requested.

Help ensure our AI infrastructure is built with security, privacy, and responsible data management in mind.

You’re a builder.

You enjoy taking something that works in a development environment and figuring out how to make it reliable in production.

You’re comfortable working across data engineering, cloud infrastructure, DevOps, and AI systems.

You don’t need everything perfectly defined before you start. You can work through ambiguity, identify what needs to be built, and create practical solutions.

You care about reliability and performance, but you also understand that technology needs to support real business and customer outcomes.

You’re comfortable working independently, but you know when to collaborate, ask questions, and bring others into a decision.

Most importantly, you enjoy solving difficult technical problems and building the infrastructure that allows ambitious ideas to become real products.

Experience with Model Context Protocol (MCP) or similar tool calling interfaces.

Experience with LLM evaluation and observability tools, including LLM as judge systems, tracing, and regression suites.

Experience with TypeScript and React for internal dashboards or administrative tools.

Experience working with AI products or agent based applications.