Global Head of AI Platform Engineering, SVP

Who we are looking for

Design, build, and operate enterprise AI platforms at scale, enabling secure, scalable, and high-performance capabilities across Traditional AI/ML, Generative AI, and Agentic AI—using modern engineering, Agile, and Site Reliability Engineering (SRE) practices.

The Head of AI Platform Engineering is accountable for delivering and operating enterprise-grade AI platforms that enable State Street to develop, deploy, and scale AI capabilities across all businesses and functions.

This is a deep engineering leadership role, leading a global organization of 100+ engineers to build and run AI platforms spanning:

• Machine Learning (ML)
• Generative AI (LLMs and foundation models)
• Agentic AI systems and orchestration frameworks

The role combines:

• Advanced AI/ML and distributed systems engineering
• Platform product mindset (platforms as reusable services)
• SRE discipline (reliability, observability, scalability)
• Agile execution (rapid iteration and continuous delivery)

The role works in close partnership with:

• Data Platform Engineering to leverage AI-ready data foundations
• Data Architecture to align with enterprise data models and structures
• Data & AI Strategy, Portfolio & Value to align with enterprise priorities and roadmap
• Responsible Data, AI Governance & Risk to ensure compliant and responsible usage

This leader is central to enabling a scalable, reusable AI ecosystem across Investment Services, Investment Management, Wealth, Alpha, Global Markets, and control functions.

Success is measured by platform adoption, engineering quality, scalability, performance, and the ability to accelerate AI innovation across the enterprise.

What you will be responsible for

Enterprise AI Platform Engineering

• Design, build, and operate AI platforms as enterprise products, including:
  • ML development, training, and inference platforms
  • Generative AI platforms (LLM integration, orchestration, prompt systems)
  • Agentic AI frameworks and runtime environments
• Own the full lifecycle:
  • Platform engineering and development
  • Deployment and operations
  • Continuous optimization and evolution

Large-Scale Engineering Leadership (100+ Organization)

• Lead a global organization of 100+ engineers across:
  • AI/ML platform engineering
  • LLM and GenAI engineering
  • Agentic AI and workflow orchestration
  • Platform reliability engineering
• Build strong leadership layers and domain-aligned teams
• Drive a culture of:
  • Engineering excellence
  • Innovation with discipline
  • Ownership and accountability

Site Reliability Engineering (SRE) & AI Platform Operations

• Establish and embed SRE practices across AI platforms:
  • SLAs, SLOs, and error budgets
  • Observability across models and pipelines
  • Incident management and operational playbooks
• Ensure production-grade reliability for:
  • Model training and inference
  • API-based AI services
  • Agent-based systems
• Automate monitoring, scaling, and recovery for AI workloads

Agile Delivery & Platform Product Mindset

• Implement modern Agile and product-centric engineering practices
• Manage platforms as products, including:
  • Roadmap alignment with enterprise strategy
  • Continuous delivery and iteration
  • Feedback loops from users (engineers, data scientists, product teams)
• Drive disciplined execution through:
  • Backlog prioritization
  • Sprint-based delivery
  • Outcome-based measurement

AI/ML, GenAI & Agentic AI Platform Capabilities

• Deliver and evolve platforms across:
  • ML platforms (experimentation, training, deployment, feature pipelines)
  • Generative AI platforms (LLM orchestration, prompt management, evaluation)
  • Agentic AI platforms (multi-agent systems, task orchestration, automation workflows)
• Support both:
  • Centralized enterprise capabilities
  • Domain-specific customization

Integration with Data Platforms

• Leverage enterprise data platforms to enable:
  • High-quality training datasets
  • Feature engineering pipelines
  • Access to structured and unstructured data
• Ensure tight integration of:
  • Data pipelines
  • Feature stores
  • Vector and embedding data system