Infrastructure Engineer; AI Platforms

Who We Are: Galaxy is a global leader in digital assets and data center infrastructure, delivering solutions that accelerate progress in finance and artificial intelligence. We believe that blockchain and digital asset innovation will transform how value moves through the world – and we’re building the products and services to make that future a reality. Our institutional digital assets platform spans trading, investment banking, asset management, staking, self‑custody, and tokenization technology.

We also invest in and operate cutting‑edge data center infrastructure to power AI and high‑performance computing, addressing the growing demand for scalable energy and compute in the U.S. We work at the intersection of finance and technology, helping institutions, startups, and developers navigate a digitally native economy. Led by CEO and Founder Michael Novogratz, our team blends deep crypto expertise with institutional experience and a shared commitment to shaping the future of Web3 and AI.

Galaxy is headquartered in New York City, with offices across North America, Europe, the Middle East, and Asia.

• Seek Excellence.
• Be Selective To Be Effective.
• Be Highly Aligned, Loosely Coupled.
• Disagree Transparently.
• Encourage Independent Decision‑Making.
• Build Dream Teams.

We are building a large‑scale, enterprise‑grade AI platform that supports advanced machine learning systems end‑to‑end—from model development and training to secure, reliable, production deployment. We are looking for a senior infrastructure leader who has designed and operated modern ML platforms at scale and understands the operational, security, and reliability challenges of real‑world AI systems.

This role sits at the intersection of infrastructure engineering, MLOps, Dev Ops, and security, with a mandate to build systems that are fast for developers, safe for the business, and resilient in production.

• Architect and own the core infrastructure for AI and ML workloads, including training, inference, experimentation, and evaluation.
• Design and operate Kubernetes‑based platforms for scalable, reliable, and cost‑efficient AI workloads.
• Build and evolve MLOps pipelines: model training, versioning, deployment, monitoring, rollback, and lifecycle management.
• Establish best practices for Dev Ops and CI/CD across data, ML, and application layers.
• Lead security and compliance for AI systems: secrets management, access controls, isolation, auditability, and supply‑chain security.
• Ensure high availability, observability, and incident response for production AI services.
• Partner closely with ML engineers, researchers, and application teams.
• Set technical direction, review designs, and mentor engineers.

• 10+ years of experience in infrastructure, platform, or systems engineering.
• Deep, hands‑on experience with Kubernetes in production.
• Strong background in MLOps and ML platform operations.
• Experience across Dev Ops, CI/CD, and cloud infrastructure (AWS, GCP, or Azure).
• Proven ability to design secure, enterprise‑grade systems.
• Proficiency in Go, Python, Java, or similar.

• Experience supporting GPU‑accelerated workloads.
• Familiarity with security and compliance frameworks.
• Experience building internal platforms for multiple teams.
• Prior work on enterprise‑facing AI platforms.

• Own and shape the foundation of a next‑generation AI platform.
• High‑impact, high‑autonomy role with architectural authority.
• Competitive compensation and long‑term growth.

The base salary ranges included below will be commensurate with candidate experience, expertise and local market. Final offer amounts are determined by multiple factors, including candidate experience and expertise. At Galaxy, we maintain a total compensation philosophy which consists of a competitive base salary, annual bonus, and equity incentives.

Base Salary Range: $220,000—$250,000 USD

Galaxy respects diversity and seeks to provide equal employment opportunities to all employees and job applicants for employment without regard to actual or perceived age, race, color, creed, religion, sex or gender (including pregnancy, childbirth, lactation and…