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.