TC
Full-Stack AI Engineer — Platform Architect
TechDigital Corporation
🇺🇸 United States
Hybrid
Staff / Principal
1 month ago
- AI
- MLflow
- OpenTelemetry
- vLLM
- Ray
- CI/CD
- IaC
- Kubernetes
- Terraform
- RBAC
- GCP
- GKE
- Vertex AI
- IAM
- VPC
- BigQuery
- Machine Learning
- MCP
- RAG
- Cursor
- Claude Code
- Copilot
- Python
- Java
- TypeScript
1 month ago
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 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.
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).
Full-Stack AI Engineer — Platform Architect · TechDigital Corporation