Head of AI Platform

Schonfeld•New York, NY

1d•$275,000 - $300,000

About The Position

Schonfeld is seeking a Head of AI Platforms to lead the strategy, architecture, engineering, and operation of the firm’s shared AI technology platform. The role 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. The position owns the AI platform roadmap and oversees delivery across model access and serving, agent frameworks, enterprise connectivity, developer tooling, evaluation, observability, and supporting infrastructure. Working with AI application engineers, quantitative researchers, portfolio managers, infrastructure, cybersecurity, and technology leadership, the role translates business priorities into reusable platform capabilities while maintaining strong engineering and operational standards. Schonfeld is a global multi-manager hedge fund seeking to deliver industry-leading risk-adjusted returns. It invests across Quant, Tactical, Fundamental Equity, and Discretionary Macro & Fixed Income strategies, drawing on internal and external portfolio manager teams, proprietary technology, infrastructure, and risk analytics. The firm describes its culture as collaborative and teamwork-oriented, with learning and educational offerings and opportunities to make an impact. The expected base pay is $275,000–$300,000. The role may also be eligible for a performance bonus and a competitive benefits package. Actual compensation depends on factors including skills, qualifications, and experience.

Requirements

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.
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 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 engineering, observability, cybersecurity, identity, and access management.
Excellent technical judgment, prioritization, and communication skills, with ability to influence senior stakeholders.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical discipline, or equivalent practical experience.

Nice To Haves

Experience in hedge funds, quantitative investment management, trading, or financial services.
Experience building enterprise AI platforms, internal developer platforms, or shared services with broad organizational adoption.
Hands-on experience with agent frameworks, MCP servers and gateways, AI evaluation, and MLOps/LLMOps.
Familiarity with high-performance computing, NVIDIA GPU infrastructure, and inference optimization.
Experience managing technology vendors, platform budgets, and infrastructure costs.
Master’s degree or Ph.D. in a relevant technical field.

Responsibilities

Define and execute the 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, balancing capabilities, integration requirements, cost, and operational risk.
Establish platform priorities, service ownership, and measures of success for adoption, reliability, developer productivity, and cost.
Own end-to-end architecture for shared AI services, including model gateways, inference, agent runtimes, orchestration, retrieval, and enterprise data integration.
Lead 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 supporting 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.
Provide internal engineering teams with documentation, onboarding, tooling, and support as platform customers.
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.