Geared toward senior technical leaders comfortable with building enterprise AI platforms and managing cross-functional collaboration.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Schonfeld via Greenhouse
The Role
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.
Key Responsibilities
Platform Strategy and Ownership
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.
Architecture and Engineering
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.
Reliability, Security, and Quality
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.
Team Leadership and Platform Adoption
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.
Required Qualifications
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 engineering, observability, cybersecurity, identity, and access management.
Excellent technical judgment, prioritization, and communication skills, with the ability to influence senior stakeholders.
Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical discipline, or equivalent practical experience.
Preferred Qualifications
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.
Who we are
Schonfeld is a global multi-manager hedge fund that strives to deliver industry-leading risk-adjusted returns for our investors. We leverage both internal and external portfolio manager teams around the world, seeking to capitalize on inefficiencies and opportunities within the markets. We draw from decades of experience and a significant investment in proprietary technology, infrastructure and risk analytics to invest across four main strategies: Quant, Tactical, Fundamental Equity and Discretionary Macro & Fixed Income.
Our Culture
At Schonfeld, we’ll invest in you. Attracting and retaining top talent is at the heart of what we do, because we believe that exceptional outcomes begin with exceptional people. We foster a culture where talent is empowered to continually learn, innovate and pursue ambitious goals. We are teamwork-oriented, collaborative and encourage ideas—at all levels—to be shared. As an organization committed to investing in our people, we provide learning and educational offerings and opportunities to make an impact. We encourage community through internal networks, external partnerships and service initiatives that promote inclusion and purpose beyond the firm’s walls.
The base pay for this role is expected to be between $275,000 and $300,000. The expected base pay range is based on information at the time this post was generated. This role may also be eligible for other forms of compensation such as a performance bonus and a competitive benefits package. Actual compensation for the successful candidate will be determined based on a variety of factors such as skills, qualifications, and experience.
__PRESENT
__PRESENT
Source: Schonfeld careers (Greenhouse)