Platform Architect (New York)

Location: New York, NY (Hybrid, 3 days onsite per week)

Job Description: -The most senior engineer on the team and the technical anchor for the platform. You design the reference architecture and personally build the hardest pieces — the lifecycle registries, the AI control-plane services that plug into the enterprise gateway, and the harness/memory services — setting the patterns the rest of the pod extends.

What You Will Do: -Own the paved road — the reference architecture and the reusable registration pattern behind every asset (registration schema, semantic metadata, I/O contract, execution binding, governance constraints) so the agent, model, tool, workflow and skill registries all share one shape. Personally build the hardest pieces — the lifecycle registries; the AI control-plane services that plug into the enterprise gateway (registries, PII/PHI and prompt-injection scanning as a gateway policy, token-aware metering and multi-vendor cost attribution, AI trace/observability, and the multi-provider model-abstraction layer behind its fail-over); and the harness/memory & skills services. Build the agent-building paved road — the scaffolds, patterns and sub-agent topologies the team uses to stand up new agents fast, including the end-to-end builder agent (architect → provision → implement → test sub-agent → deploy → post-process → log).Deliver platform features as agents where it fits (e.g. a lifecycle-management agent, a skills-planning agent) — so the platform builds and operates itself, not just exposes CRUD APIs. Enforce portability in code — containerize everything; standardize lineage on MLflow + metadata, telemetry on Open Telemetry, table formats on Delta UniForm / Iceberg, and open-weight serving on vLLM/Ray/DeepSpeed. Set the engineering bar — testing, CI/CD, IaC and security-by-default standards; review the team's hardest designs and pull requests; mentor the senior engineers. Build with AI and build agents end to end — like everyone on the team.

What We're Looking For: Required Qualifications-Principal-level full-stack — you build production services end to end (backend, APIs, and enough frontend to ship the registry and dev UIs) and you own systems in production. Distributed systems & platform engineering — Kubernetes, containers, IaC (Terraform), CI/CD, secrets/RBAC, and multi-tenant service design. Cloud depth with a portable mindset — strong on GCP (GKE, Vertex AI, IAM, VPC Service Controls, BigQuery), but standards-first by instinct. Hands-on GenAI / agentic engineering — LLM and agent runtimes, multi-agent and sub-agent orchestration, A2A and MCP/tool integration, retrieval/RAG, memory systems, and end-to-end builder agents. Security & governance by design — identity-aware access, PII/PHI handling, runtime guardrails, gateway/policy-as-code, and audit/observability. AI-assisted engineering — fluent and effective with AI coding tools (Cursor, Claude Code, Copilot, Windsurf or equivalent), and able to define the patterns and review discipline the team uses with them. Strong software engineering background — strong Python (and typically one of Go / Java / TypeScript).Ideal Traits-Builds reusable, well-documented platform primitives that other engineers want to build on. Systems thinker; comfortable with ambiguity and incomplete requirements. Pragmatic about AI hype; reliability- and value-focused. Generous mentor; raises the bar by example. Why This Role-Maximum technical autonomy — you own the hardest, highest-leverage parts of a brand-new platform and the patterns everything else inherits. Staff-level impact without management overhead — a first-class IC track, paid at the top of the market, anchoring a team of strong engineers. Genuinely hard problems — AI control planes, multi-agent governance, enterprise memory, and builder agents done right. Success Looks Like-Reusable paved roads (registries, harness, builder agents) the whole team builds on. The hardest platform pieces shipped and reliable in production. A high, consistent engineering bar across the pod.