Senior AI Platform Engineer
Builds and maintains AI platform infrastructure for agentic systems, including connectors, execution environments, governance, and self-service tools to enable safe, scalable AI use across engineering and business teams. Requires 8+ years experience with LLM agents, GCP, and cloud-native tech.
About the job
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
Skills
Agentic AI, Llm Workflows, LangGraph, Claude Sdk, Google Agent Development Kit, GCP, Kubernetes, OAuth, Infrastructure-As-Code, Vertex Ai Agent Builder, Cursor, Claude Code
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