Director, Enterprise AI Platform Architect

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Director, Enterprise AI Platform Architect based in United States. This is a hands-on enterprise AI architecture role focused on turning emerging AI capabilities into measurable business outcomes.

You will work directly with technology and product teams across a broad portfolio of companies to identify, prioritize, and deliver high-value agentic AI use cases.

The role combines generative AI expertise, platform architecture, rapid prototyping, and production deployment.

You will help teams move from proof-of-value initiatives to scalable, secure, and resilient enterprise solutions.

A major focus will be establishing reusable architectures, governance standards, and best practices for AI platforms and agentic systems.

You will also assess AI maturity, evaluate emerging technologies, and contribute technical insight to strategic and investment decisions.

Success requires someone who can operate deeply in the technology while communicating effectively with executives and diverse engineering teams.

Accountabilities

• Partner with technology and product teams to identify and prioritize agentic AI opportunities that align with product roadmaps and business objectives.
• Lead end-to-end delivery of AI agent solutions, using rapid development approaches to achieve MVPs and measurable proof-of-value outcomes within compressed timelines.
• Guide AI solutions from prototype through production deployment, helping teams establish scalable and reliable operating models.
• Conduct AI maturity assessments, identify capability gaps, and develop tailored roadmaps for improvement.
• Perform architectural reviews and recommend improvements related to performance, scalability, security, resilience, and maintainability.
• Design enterprise AI platforms across AWS, Azure, and GCP, using appropriate abstraction layers to support vendor independence.
• Develop and maintain reference architectures covering generative AI applications, agentic systems, and multi-agent collaboration patterns.
• Establish appropriate autonomy and human-in-the-loop controls based on risk, regulatory requirements, and business context.
• Define standards for AI development, deployment, monitoring, lifecycle management, privacy, model provenance, governance, and auditability.
• Serve as a technical authority on generative AI, LLMs, RAG, autonomous agents, and multi-agent architectures.
• Evaluate emerging AI technologies and assess their potential impact on product strategies, technology roadmaps, and investment priorities.
• Develop and promote reusable AI platform design, development, and deployment practices across multiple organizations.
• Support technical due diligence by assessing AI capabilities, technical debt, architecture, and readiness for agentic AI adoption.
• Translate complex technical findings into clear recommendations for executives, technology leaders, and other stakeholders.
• Collaborate across multiple companies and teams simultaneously while maintaining strong delivery focus and technical quality.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, or a related technical discipline.
• At least 3 years of experience in AI/ML platform architecture and development, including 2+ years of recent hands-on experience with generative AI and agentic architectures in production applications.
• Demonstrated success delivering AI applications into production environments rather than limiting work to prototypes or research.
• Strong expertise in generative AI technologies, including LLMs, RAG architectures, diffusion models, prompt engineering, and model fine-tuning.
• Deep understanding of agentic architectures, autonomous agents, multi-agent systems, and enterprise AI application patterns.
• Strong knowledge of public cloud AI services across AWS, Azure, and GCP, with the ability to design secure, scalable, and vendor-agnostic platforms.
• Solid foundations in machine learning and deep learning, as well as AI model development, deployment, and lifecycle management.
• Experience designing platform abstraction layers and architectures that avoid unnecessary vendor dependency.
• Ability to operate effectively in fast-paced, ambiguous environments and deliver proof-of-value solutions on compressed timelines.
• Strong analytical, pr…