AI Platform Engineer

Accountabilities
The AI Platform Engineer will be responsible for building and operating the infrastructure that enables reliable, measurable, and governed AI systems across the organization. Design, build, and maintain the AI execution platform, including event-triggered workflows, model-agnostic runtimes, durable state management, human review processes, rollback mechanisms, and comprehensive run logging. Own the architecture decisions required to bring AI agent systems into production and ensure the platform operates reliably at scale. Develop the evaluation layer that determines whether AI systems are safe, accurate, and ready for production use. Create golden test suites, behavioral evaluations, safety checks, and CI gates that prevent unreliable AI capabilities from being deployed. Build and manage a portfolio of AI agents supporting business workflows such as reporting, drafting, analysis, triage, and knowledge management. Develop meta-level systems that monitor agent performance, identify improvements, and continuously enhance the platform. Implement governance mechanisms directly into the platform, including risk tiers, least-privilege permissions, tool access controls, audit logging, and human approval workflows. Ensure high-risk AI actions are technically restricted through system design rather than relying only on policies or prompts. Design responsible communication systems that optimize notifications, minimize interruptions, and increase user trust and adoption. Build platform tooling, including validators, compilers, context distribution systems, and integrations across repositories and collaboration environments. Create data pipelines and reporting systems that measure AI adoption, operational impact, maturity levels, and return on investment. Instrument AI workflows to track usage, efficiency gains, costs, and measurable business value. Requirements The ideal candidate is an experienced AI platform engineer with a strong background in production-grade AI systems, infrastructure ownership, and reliable software engineering practices. Proven experience building and deploying production LLM agent systems used by real users, beyond prototypes or demonstrations. Strong understanding of AI evaluation methodologies, including golden datasets, behavioral assertions, judge criteria, safety testing, and automated quality gates. Experience designing and implementing reliable AI workflows where correctness, traceability, and governance are critical. Deep API integration experience with business systems and experience creating MCP servers. Strong infrastructure engineering skills using Python, cloud platforms such as GCP, cloud data warehouses, and infrastructure-as-code tools. Ability to independently design, deploy, monitor, troubleshoot, and improve production systems end to end. Experience with LLM observability, tracing, cost tracking, and transforming AI execution data into actionable insights. Strong understanding of security principles, access controls, permissions management, and system-level risk prevention. Experience building internal platforms, developer tools, or automation systems that achieved strong adoption among non-engineering teams. Strong product mindset and ability to design AI experiences that respect user attention and build trust. Experience working with agentic development tools and the ability to explain workflows, permissions, approval points, logging strategies, and lessons learned from failures. Public contributions such as open-source AI frameworks, MCP servers, evaluation tools, or technical writing in AI reliability are considered a strong advantage. Benefits Fully remote opportunity with the flexibility to work from anywhere in the world. Remote-first culture with coworking support through a WeWork membership or coworking allowance. Employee equity ownership program. Technology allowance to create an ideal home office setup with equipment of your choice. Comprehensive health benefits, including full employee health insurance coverage and dependent coverage support. Annual company off-site gatherings focused on collaboration, connection, and team building. Flexible and asynchronous work environment built on trust and autonomy. Annual professional development budget for courses, books, conferences, and learning opportunities. Opportunity to work with a globally distributed team across multiple countries and cultures. Chance to contribute to open-source-driven products used by a large developer community. High-impact role with significant ownership over AI infrastructure strategy and execution.

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We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.
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