Head of AI Platform Schonfeld ago

We are seeking a Head of AI Platforms to lead the strategy, architecture, engineering, and operation of the firm's shared AI technology platform. You will build the foundations that enable research, investment, and business teams to develop and deploy AI applications securely, reliably, and at scale.

This is a hands-on technical leadership role combining platform ownership, architectural direction, and team leadership. You will own the AI platform roadmap and oversee delivery across model access and serving, agent frameworks, enterprise connectivity, developer tooling, evaluation, observability, and supporting infrastructure.

Working closely with AI application engineers, quantitative researchers, portfolio managers, infrastructure, cybersecurity, and technology leadership, you will translate business priorities into reusable platform capabilities that accelerate delivery while maintaining strong engineering and operational standards.

• Define and execute the firm's AI platform strategy and roadmap, aligning investments with research, investment, and operational priorities.
• Own the platform lifecycle, from technology evaluation and architecture through implementation, adoption, and ongoing operation.
• Lead build-versus-buy decisions across a rapidly evolving vendor landscape, balancing capability, integration requirements, cost, and operational risk.
• Establish clear platform priorities, service ownership, and measures of success across adoption, reliability, developer productivity, and cost.

• Own the end-to-end architecture for shared AI services, including model gateways, inference, agent runtimes, orchestration, retrieval, and enterprise data integration.
• Lead the development of agent frameworks, developer tooling, MCP servers and gateways, and reusable tools, plugins, and skills.
• Establish reference architectures, APIs, engineering standards, and deployment patterns that support interoperability, scalability, and maintainability.
• Guide infrastructure decisions across cloud, on-premises, and hybrid environments, including Kubernetes and GPU-backed workloads.
• Remain hands-on in critical design decisions, prototyping, code reviews, and complex implementation challenges.

• Ensure platform services meet defined expectations for availability, performance, resilience, and support.
• Embed authentication, authorization, entitlements, secrets management, and data-access controls throughout the platform.
• Build evaluation and observability capabilities that make AI applications and agents testable, traceable, and safe to release.
• Establish standards for automated testing, CI/CD, versioning, dependency management, and software supply-chain security.
• Partner with cybersecurity, risk, and compliance teams to support auditability, appropriate human oversight, and controlled access to sensitive information.

• Build, lead, and mentor a high-performing AI platform engineering team.
• Set technical direction, prioritize delivery, and establish clear accountability across platform components.
• Partner with AI application teams to turn recurring solution patterns into reusable, production-ready platform capabilities.
• Treat internal engineering teams as platform customers, providing effective documentation, onboarding, tooling, and support.
• Communicate technical trade-offs, delivery progress, and investment recommendations to senior stakeholders.
• Drive standardization and automation to reduce technical debt, operational risk, and infrastructure complexity.

• 10+ years of experience in software engineering, platform engineering, infrastructure, or distributed systems, including senior technical leadership.
• Demonstrated experience leading engineering teams and owning shared platforms or infrastructure used by multiple teams.
• A track record of taking complex technical capabilities from initial design through production deployment and ongoing operation.
• Strong understanding of LLMs, agentic systems, retrieval-augmented generation, model serving, and AI application architectures.
• Hands-on software development expertise, particularly in Python, and the ability to evaluate designs and contribute to critical implementations.
• Deep experience with distributed systems, APIs, Kubernetes, containerization, cloud or hybrid infrastructure, and infrastructure automation.
• Strong understanding of reliability…