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About 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.

Responsibilities

Define and execute the firm’s AI platform strategy and roadmap.
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.
Establish clear platform priorities, service ownership, and measures of success.
Own the end-to-end architecture for shared AI services.
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.
Guide infrastructure decisions across cloud, on-premises, and hybrid environments.
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.

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.
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.
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.

Skills

Python
LLMs
Agentic systems
Retrieval-augmented generation
Model serving
AI application architectures
Distributed systems
APIs
Kubernetes
Containerization
Cloud infrastructure
Hybrid infrastructure
Infrastructure automation
Reliability engineering
Observability
Cybersecurity
Identity management
Access management
Agent frameworks
MCP servers
MCP gateways
AI evaluation
MLOps
LLMOps
High-performance computing
NVIDIA GPU infrastructure
Inference optimization

Experience Level

Senior technical leadership
10+ years of experience

Education Level

Bachelor’s degree in Computer Science, Engineering, Mathematics, or a related technical discipline, or equivalent practical experience.
Master’s degree or Ph.D. in a relevant technical field.

Salary/Compensations

$275,000 to $300,000
Performance bonus

Benefits

Competitive benefits package

About the Company

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.