Principal AI/ GenAI Platform Architect ONSITE- Austin| San Francisco| Los Angeles| Chicago - W2 Only

Location: Austin, TX | San Francisco, CA | Los Angeles, CA | Minneapolis, MN | Chicago, IL (ONSITE). Duration: 24+ Months W2 Only. Work authorization: Any work authorization can apply. Level: Senior Technical Lead / Architect / Director-mapped Player-Coach.

About the role

We are seeking a Senior AI/GenAI Platform Architect / Technical Lead who started their career as a hands-on Software Engineer and has evolved into designing and leading enterprise AI/ML and Generative AI platforms. This is not a pure Data Scientist, ML Researcher, AI Strategy, or management-only position. The ideal candidate has a strong software engineering foundation in Python, Java, Go and/or TypeScript/React, distributed systems, APIs and cloud-native architecture, combined with recent hands-on experience building enterprise AI/GenAI platforms and agentic systems in production. We are particularly interested in candidates who have built AI platforms or products from the ground up, rather than primarily supporting existing environments. Experience working with Fortune 500 enterprises and complex or highly regulated environments is strongly preferred. A combination of large-enterprise and startup/product-building experience is especially valuable.

Responsibilities

• Own the architecture and technical direction of an enterprise AI/GenAI platform.
• Define the reference architecture for the AI control plane, shared platform services, SDKs, templates and developer experience.
• Architect and build model gateways supporting multiple AI providers such as Azure OpenAI, Amazon Bedrock, Vertex AI and other LLM providers.
• Implement model routing, failover, quotas, rate limiting, tenant isolation, key management, token accounting and cost allocation.
• Design and build enterprise GenAI runtimes supporting LLM applications, RAG, context and memory management, vector/hybrid search and tool execution.
• Architect agentic AI platforms, including agent, model, prompt and tool registries, versioning, lineage, approvals and promotion processes.
• Implement MCP-style tool interoperability and secure integration between AI agents and enterprise systems.
• Build AI observability and traceability covering prompts, model calls, tool calls and agent execution.
• Establish evaluation frameworks for AI quality, hallucination/accuracy, drift, latency, reliability and cost.
• Implement enterprise AI security and guardrails, including PII/PHI protection, prompt-injection defenses, identity, least-privilege access and policy enforcement.
• Establish platform engineering practices using Kubernetes, infrastructure as code, CI/CD, secrets management, secure SDLC and cloud-native architectures.
• Define production SLOs, capacity and reliability standards and participate in incident management.
• Remain hands-on with production code, architecture reviews, prototypes and critical technical components.
• Make pragmatic build-vs-buy decisions and evaluate AI platform technologies and vendors.
• Partner with Product, AI Engineering, Data Science, Security and enterprise application teams.
• Initially operate as a player-coach and help build and lead a platform engineering team of approximately 3‑8 engineers.

Requirements

• 10‑15+ years of experience across software engineering, platform engineering, distributed systems, cloud architecture or related engineering disciplines.