Senior AI Platform Infrastructure Engineer

Project description

Our partner, a top tier Hedge Fund, is expanding their AI agent platform. The platform runs 1,500+ agents in production across the firm. We are looking for Senior Platform Engineers to build across the whole stack, from the infrastructure to the agents and MCP tools that run on it. In the next phase agents securely work together.

Responsibilities

• You'll join the team that runs and own both layers: the agents, MCP servers and tools our users rely on, and the Kubernetes/AWS platform with event-driven systems underneath them. From there, you'll build what lets agents work as a team: secure communication between agents, agents that manage other agents, shared memory with the right access controls, and the evals that keep it reliable at scale.

SKILLS

Must have

• Hands-on experience building agents in production with agent SDKs (e.g. Claude Agent SDK, Codex SDK): tool use, context and memory, prompting
• Experience building and running MCP servers and tools Deep hands-on Kubernetes (EKS preferred): cluster design, networking (CNI, service mesh), RBAC and security policies
• Strong AWS: VPC architecture, IAM, EKS/ECS, networking and security groups Infrastructure as code (Terraform, CloudFormation or Pulumi), GitOps and CI/CD (ArgoCD, GitHub Actions, Jenkins)
• Container architectures for multi-tenant, security-sensitive workloads
• Event-driven systems: queues and pub/sub (e.g. SQS, Kafka), webhooks, async workers, retries and idempotency
• Production observability and incident response (Datadog or similar)
• Strong python

Nice to have

• Agent-to-agent (A2A) protocols and multi-agent orchestration • JavaScript/TypeScript and Go • UI / front-end experience (a plus for one of the three roles: agent management console) • Auth: OAuth 2.0 / OIDC, identity federation and SSO, delegated (on-behalf of) tokens • Agent evaluation: test sets, graders, regression gates • Permission-aware search, retrieval or shared memory • Securing autonomous or long-running AI workloads at scale • Financial or regulated environments, compliance-aware infrastructur