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 for these openings.

Education Requirement - Bachelor's Degree in: Computer Science, Information Technology, Or related field

Level: Senior Technical Lead / Architect / Director-mapped Player-Coach

Position Overview

• 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.

• 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.

• 10 15+ years of experience across software engineering, platform engineering, distributed systems, cloud architecture or related engineering disciplines.
• Strong early-career and continuing foundation as a hands-on Software Engineer.
• Significant recent experience architecting or building enterprise AI, GenAI or agentic AI platforms in production.
• Strong hands-on development experience with Python plus at least one of Java, Go or TypeScript.
• Strong understanding of APIs, microservices, distributed systems, testing, code reviews and secure software development.
• Hands-on understanding of LLMs, model gateways, RAG, vector databases/search, embeddings, agents, prompts, tool calling and AI evaluation.
• Experience with AI observability, tracing, governance and guardrails.
• Strong cloud experience with Azure, AWS and/or GCP.
• Experience with Kubernetes, containers, CI/CD and infrastructure as code.
• Experience designing highly available, secure and scalable production platforms.
• Demonstrated ability to make architecture decisions and communicate with senior technical and business stakeholders.
• Ability to remain technically hands-on while providing leadership to engineering teams.

Thanks & Regards,

Namrata Ahuja | Lead Talent Acquisition - US Staffing

T View phone number on us.fitly.work | View email address on us.fitly.work

#LI-NA1