Principal AI Engineer

About the role

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