Senior AI Platform Engineer

Responsibilities

• Own the connector and service integration layer that powers AI workflows across the company.
• Design and ship execution environments for agents and higher-autonomy AI workflows, including isolation boundaries and access controls.
• Build reusable platform services, golden paths, and self-service templates that reduce setup friction for teams building on AI.
• Productize onboarding so it works reliably for both developers and non-developers without depending on manual intervention or tribal knowledge.
• Define and enforce technical standards for agent execution, evaluation loops, and deployment.
• Partner with Security and IT to ship deployable patterns for higher-risk AI capabilities.
• Own the AI governance layer: access controls, audit trails, approval criteria, and deployment boundaries for agentic workflows.
• Set the reliability, observability, and operational bar for AI-specific infrastructure.
• Act as the technical escalation point when onboarding or platform issues block rollout.
• Reduce the company's dependence on individual heroics by turning exception handling into repeatable paths.

Requirements

• 8+ years in software, platform, infrastructure, or adjacent engineering roles.
• Hands-on experience building agentic AI systems: LLM-powered workflows, tool-calling agents, evaluation loops, or autonomous execution — using frameworks like Claude SDK, Google Agent Development Kit (ADK), LangGraph, or similar. Not classical ML or data pipelines.
• Direct experience with GCP. We run on Google Cloud.
• Strong experience with APIs, auth, OAuth, secrets, CLI tooling, and deployment patterns.
• Cloud-native systems experience with containers, orchestration (Kubernetes), and infrastructure-as-code.
• Experience implementing AI governance controls: access boundaries, audit logging, approval workflows, and safe deployment standards for higher-autonomy systems.
• Comfortable operating in both fast-moving, low-process environments and more structured, compliance-aware ones.
• Strong bias toward simplification, standardization, and operational reliability over clever one-off solutions.
• Excellent communication skills with the ability to work across engineering, security, and non-technical stakeholders.

Nice-to-Haves

• Shipped production agentic systems with real external tool access (filesystems, APIs, staging systems).
• Hands-on experience with Google Agent Engine (Vertex AI Agent Builder) or equivalent managed agent execution platforms.
• Direct experience with AI-native coding environments (e.g. Cursor, Claude Code).
• Designed or operated agent sandboxing, isolation, or evaluation frameworks.
• Built self-service developer platforms or golden paths used by multiple teams.
• Has startup experience and knows how to build durably with limited resources.
• Familiarity with fintech, regulated environments, or compliance-aware deployment.

Compensation & Benefits

• Competitive Salary & Stock Options
• Health Benefits
• New Hire Home-Office Setup: One-time USD $500
• Monthly Stipend: USD $150 per month via a Brex Card