MLOps / ML Platform Engineer - SumerSports LLC

SumerSports is a leading football intelligence technology company that specializes in providing an innovative suite of products for football fans and NFL clubs. We are a collection of executives, engineers, data scientists, and visionaries from NFL clubs, technology startups, finance, and academia.

Our data-driven platform empowers teams with insights and tools to make informed decisions within salary cap constraints. The platform also serves the NCAA, offering insights around the transfer portal and more.

What sets us apart is our unique blend of big tech talent, data scientists, and former NFL personnel, who have a combined 600+ years of NFL experience. Our domain knowledge is augmented by AI and machine learning technologies to create a unique view into many aspects of Football.

As an MLOps/ML Platform Engineer, you’ll build and operate the core systems that power our machine learning and AI workloads across sports domains. You’ll own the infrastructure that keeps our models fast, reliable, and cost-efficient — from data ingestion and training to model serving, deployment, and observability.

This is a hands-on engineering role that blends software infrastructure, distributed systems, and machine learning productionization. You’ll work closely with our Deep Learning Research, LLMOps, and Product Engineering teams to ensure that every model we build can be trained, deployed, and monitored at scale.

Responsibilities:

• Design and operate ML infrastructure: Manage data, training, serving, and inference systems for high-throughput model workflows.
• Build scalable pipelines: Implement reproducible training and evaluation pipelines with versioning, scheduling, and artifact tracking.
• Optimize compute and cost: Tune GPU and CPU workloads, manage clusters, and drive efficiency via rightsizing, spot scheduling, and caching.
• Serve models in production: Operate APIs for low-latency inference with autoscaling, blue-green or canary rollouts, and rollback safety.
• Ensure reliability and observability: Define and own SLOs; instrument pipelines and services to track latency, cost, drift, and data quality.
• Secure and automate: Manage IAM, secrets, and container security; automate deployment pipelines via CI/CD and infrastructure as code.
• Collaborate cross-functionally: Partner with research scientists and AI engineers to deliver models from experiment to production with minimal friction.
• Document and enable: Build templates, runbooks, and internal tooling that make ML workflows repeatable, safe, and fast.

Qualifications:

• 4+ years of experience in ML platform, DevOps, or infrastructure engineering.
• Deep knowledge of Kubernetes, CI/CD, containers, and cloud infrastructure (AWS, GCP, or Azure).
• Hands-on experience managing GPU clusters and training/inference pipelines.
• Familiarity with data orchestration and storage formats