Staff Platform Engineer, AI

Responsibilities

• Lead the design of AI platform infrastructure that will be rolled out across the enterprise.
• Work with stakeholders across business functions to gather requirements, evaluate technical tradeoffs, and reach consensus.
• Build and maintain AI platform infrastructure: API integrations, model access pipelines, system connectors (MCP, tool use), and on-premises compute environments as needed.
• Develop custom AI-powered applications, workflows, and integrations that connect frontier models to internal systems such as project management, documentation, communications, and operational tools.
• Own technical evaluation and selection of AI providers, tools, and platforms, making build-vs-buy decisions based on capability, compliance requirements, and speed to value.
• Act as a mentor and role model for other engineers on the team to support their growth.
• Contribute to AI training and enablement programs for the company, including hands-on workshops, office hours, documentation, and shared resources that help teams build real fluency.
• Partner with engineering, security, and IT teams to ensure AI deployments meet government security and compliance requirements across data classification levels.
• Stay current with the rapidly evolving AI landscape and bring informed recommendations on new models, tools, and capabilities that could accelerate the company.

Qualifications

• Typically 8+ years of software engineering, DevOps, or cloud engineering experience with a track record of technical leadership, including owning roadmaps, making architectural decisions, and mentoring engineers.
• Strong software engineering or similar background with experience building and shipping production systems, ideally in Python, TypeScript, or similar languages common in AI/ML tooling.
• Experience using the latest AI powered development tools to deliver production code and automate routine tasks.
• Experience with large language model APIs, including prompt engineering, tool use, retrieval-augmented generation (RAG), or agent frameworks (e.g. CrewAI, Pydantic AI).
• Demonstrated ability to work across a broad technical surface area, context-switching between infrastructure, application development, and platform architecture.
• Experience with building and using modern software development lifecycle best practices (CI/CD pipelines, continuous testing and monitoring).
• Experience deploying and operating production workloads to cloud environments (AWS, Azure, GCP).
• Strong communication skills and ability to translate technical AI concepts for non-technical audiences through training, documentation, and direct support.
• Self-directed and comfortable operating with high autonomy in a fast-paced environment where priorities shift and the playbook is being written in real time.