Principal AI Architect — Enterprise AI Platforms

Pronix Inc; United States; Hybrid; USD 180,000 - 260,000; Full time

Job summary

Pronix Inc. leads architecture across major AI platforms to govern enterprise AI initiatives.

The role focuses on defining reference architectures for Agentic AI, RAG, LLMOps, evaluation and guardrails, while guiding multi-workstream programs from discovery to delivery. The position requires deep hands-on experience with multiple AI platforms and strong emphasis on security, governance, and risk management within enterprise data environments.

Qualifications

• 10+ years engineering experience.
• 3+ years designing production GenAI/LLM systems at enterprise scale.
• Hands-on with at least two of Azure AI Foundry, Bedrock, Vertex AI, OpenAI, Anthropic.
• Experience with security, PII handling, model risk and enterprise data governance.

Responsibilities

• Own reference architectures for Agentic AI, RAG, LLMOps, evaluation and guardrails.
• Lead technical discovery, solution design and delivery governance across multi-workstream programs.
• Mentor senior engineers; represent Pronix in customer steering committees and partner councils.

Skills

GenAI/LLM systems

Cloud AI platforms

Security & governance

Tools

Azure AI Foundry

Bedrock

Vertex AI

OpenAI

Anthropic

Job description

Lead architecture across Azure AI, AWS Bedrock, Google Gemini Enterprise and OpenAI/Anthropic for enterprise AI programs.

Responsibilities

• Own reference architectures for Agentic AI, RAG, LLMOps, evaluation and guardrails.
• Lead technical discovery, solution design and delivery governance across multi-workstream programs.
• Mentor senior engineers; represent Pronix in customer steering committees and partner councils.

Requirements

• 10+ years engineering; 3+ years designing production GenAI / LLM systems at enterprise scale.
• Deep hands-on with at least two of: Azure AI Foundry, Bedrock, Vertex AI, OpenAI, Anthropic.
• Experience with security, PII handling, model risk and enterprise data governance.

Expected outcomes

• Establish reusable reference architectures that shorten delivery mobilization across programs.
• Earn trusted-advisor status with client architecture and steering committees.
• Ensure every AI program ships with enterprise-grade security, governance and evaluation in place.