A cutting-edge sports betting and analytics startup based in San Francisco is hiring a DevOps Engineer for a highly technical role focused on scaling infrastructure that supports real-time data pipelines and ML workloads.

This is a Direct Hire, Hybrid position with a salary range of $120,000 – $170,000. Crucially, this is a remote role, but candidates must reside within the Pacific Time Zone (PST) to support real-time collaboration.

Role Summary and MLOps / Containerization Mandate

This Engineer will play a critical role in scaling the infrastructure that supports real-time data pipelines for fantasy sports, betting platforms, and enterprise partners. You will collaborate directly with Data Scientists to build intelligent automation and optimized cloud systems, offering a high-impact opportunity to grow in the MLOps space.

Technical Focus Areas:

• 40% Kubernetes & Container Orchestration
• 30% AWS Cloud Infrastructure Management
• 20% CI/CD Pipeline Development & Maintenance
• 10% ML Infrastructure Support

Daily Responsibilities:

• 60% Hands-On DevOps Engineering.
• 20% Collaboration with Data Science & Engineering Teams.
• 20% Monitoring, Incident Response & On-Call Support (in a 24/7 production environment).

Required Experience and Technical Qualifications

The ideal candidate brings deep experience managing Kubernetes at scale, deploying infrastructure via Terraform, and supporting production workloads in AWS.

• Experience (Required):
  • 5+ years in DevOps, DevSecOps, or SRE roles.
  • 5+ years working with Kubernetes, Docker, and Helm.
  • 2+ years of scripting/programming in Python or Go.
  • Previous experience in 24/7 on-call production environments.
• Core Technical Stack:
  • Strong experience with AWS (EKS, EC2, RDS, etc.).
  • Proficient with Terraform or Terragrunt for infrastructure-as-code.
  • Deep CI/CD experience with tools like GitHub Actions, ArgoCD, or Jenkins.
  • Solid understanding of networking, distributed systems, and cloud security.
• Desired Skills (Bonus):
  • Experience supporting machine learning pipelines or MLOps.
  • Understanding of advanced statistics or analytics workflows.
  • Experience in a startup or high-growth environment.
  • Personal interest in sports data, betting, or fantasy.