AI Platform Lead

Role overview

Platform strategy and engineering

• Define enterprise AI platform strategy and a multi-year roadmap; evaluate emerging technologies and establish standards and reference architectures.
• Lead design, development, deployment, and operation of enterprise AI platforms and services.
• Build reusable services for model deployment, prompt management, vector search, model routing, monitoring, and evaluation.
• Ensure reliability, scalability, performance, cost optimization, production support, incident management, and operational readiness.
• Implement DevSecOps, MLOps, and AI Ops practices across the AI lifecycle.

Governance, enablement, and integration

• Establish governance frameworks, security and privacy controls, regulatory compliance, responsible-AI standards, testing, guardrails, and approval workflows.
• Implement auditability, monitoring, logging, model-performance tracking, and risk management; partner with Legal, Compliance, Risk, and Cyber Security.
• Enable governed self-service adoption; support AI assistants, copilots, predictive models, and intelligent automation.
• Create reusable patterns, templates, accelerators, and best practices; promote innovation while balancing operational risk.
• Partner with data and integration teams on governed data access, secure APIs, and integration with business applications and workflows.

Leadership

• Lead and mentor AI engineers, platform engineers, data scientists, and technical specialists; build relationships with business and technology leaders and vendors.
• Manage platform budgets, vendor relationships, licensing, and contracts; present strategy, risks, and outcomes to executive leadership.

Required qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, Information Technology, or a related field.
• 8+ years in enterprise technology, data, cloud, AI, or platform engineering roles, including 3+ years leading technical teams or enterprise platforms.
• Hands-on experience with enterprise AI, machine learning, and cloud platforms; strong experience with Azure AI Services, OpenAI technologies, ML platforms, or equivalent ecosystems.
• Experience implementing platform governance, security, and operational controls; understanding of enterprise architecture, APIs, cloud services, and data platforms.
• Strong communication, stakeholder management, and leadership skills.

Preferred qualifications

• Experience with Generative AI, Agentic AI, RAG, and LLM platforms such as Snowflake Cortex and Microsoft Copilot; MLOps and AI lifecycle management.
• Knowledge of Snowflake, Microsoft Fabric, Azure Data Services, or similar platforms; cloud certifications; regulated-industry experience.