AI Platform Engineer

AI Platform Engineer


Our client is building a platform that governs how AI agents interact with enterprise systems, including what they can access, what requires approval, and how every action is recorded. They're looking for an AI Platform Engineer to help build it from the ground up.


This is a greenfield build from an established design spec, ideal for an engineer who wants to shape a platform from day one.


Key Responsibilities

• Build the core control-plane infrastructure that enforces separation between propose, decide, and execute actions across all agents
• Implement business-rule and guardrail engines, risk-model registries, and configuration-governance workflows, including dual approval, versioning, and rollback
• Build access-control and policy-enforcement layers (OPA/Cedar-style policy engines) scoped beyond native system-of-record roles
• Implement immutable, hash-chained audit logging that satisfies enterprise compliance evidence requirements
• Build the governed connector framework, including staging, retry, and circuit-breaker logic, that mediates all writes back to systems of record
• Stand up identity and workload-identity infrastructure (SPIFFE, Entra ID federation, or similar) for agent registration and authentication
• Implement observability instrumentation (OpenTelemetry, tracing/spans) so every governed action is traceable end to end
• Build and maintain the synthetic test data environment and scenario/regression harness used to validate the platform independent of production systems
• Collaborate with the Solutions Architect to keep the platform's integration contract stable and backward-compatible as new agents onboard
• Support infrastructure provisioning and deployment automation, including containerization and CI/CD

What You Bring

• 5+ years in backend, platform, or infrastructure engineering on production systems
• Hands-on experience building policy/access-control systems, audit logging, or workflow orchestration (OPA, Cedar, Temporal, SPIFFE, or comparable tools)
• Solid working knowledge of LLMs and AI agents, including how they connect to enterprise systems and the risks they introduce
• Production experience with AWS
• Experience with containerized deployments (Kubernetes, EKS, ECS, or similar)
• Identity and authentication experience (OAuth/OIDC, Entra ID, or similar)
• Comfort building from a design spec with limited existing code
• Bachelor's degree in computer science, engineering, or a related field, or equivalent experience

Preferred Experience

• AWS certifications (Solutions Architect, DevOps Engineer, Security Specialty, or Machine Learning)
• Hands-on experience building AI agents or LLM applications
• Experience integrating enterprise middleware (ESBs, message queues) and legacy systems of record
• Familiarity with compliance-driven audit requirements (SOX ITGC, NERC-CIP, HIPAA, or similar)
• Experience building AI governance or control-tower platforms, including approval routing, risk scoring, and agent onboarding
• Background supporting critical infrastructure or highly regulated environments

Why Join

• Competitive salary and a comprehensive benefits package
• Fully remote, with flexible work arrangements that support work-life balance
• Clear paths for professional growth and career advancement
• Certification support to keep building your skills
• An inclusive, collaborative culture where your work is visible and valued

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