Principal AI Engineer

THIS IS A W2 ROLE, ANY VISA IF FINE

The rate is $65 on w2

Principal AI Engineer

• Job Title: Principal AI Engineer
• Location: New York, NY (Hybrid)
• Client: Cognizant/New York Life Insurance
• Request ID: 59948-1
• Experience Level: Principal / Staff (Individual Contributor)

Role Overview

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.

Required Skills & Qualifications

• Principal-level full-stack — build production services end to end (backend, APIs, and enough frontend to ship the registry and dev UIs) and 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

Technical Core & Responsibilities

Architecture & Platform Ownership

• 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

Agent Building & Platform Delivery

• 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

Engineering Standards & Mentorship

• Enforce portability in code — containerize everything; standardize lineage on MLflow
  • metadata, telemetry on OpenTelemetry , 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

Note

• Hybrid in New York, NY
• Principal / Staff level (Individual Contributor)
• Most senior engineer on the team — technical anchor for the platform