Site Reliability Engineer – Own Reliability -Scale ML Platform

Senior/Staff Site Reliability Engineer (SRE)

Employment Type: W2 Only

Company: StratITech

Strat

ITech is hiring a Staff Site Reliability Engineer to help our client in San Francisco scale and harden a data-intensive platform powering machine learning, neural network workloads, and real-time analytics
.

This role is built for an SRE who grew up in Linux infrastructure
, evolved through Dev Ops, and now operates at a Staff level—setting technical direction, reducing systemic risk, and raising the reliability bar across teams.

You’ll be hands-on, highly visible, and deeply embedded in how ML systems run in production.

• Build and maintain scalable Linux-based infrastructure supporting real-time analytics and ML workloads
• Improve system reliability and performance through automation, observability, and proactive capacity planning
• Own CI/CD pipelines
  , deployment automation, rollback strategies, and configuration management for production systems
• Implement and operate monitoring, alerting, SLOs, runbooks, and incident response processes for critical services
• Partner with engineering and data science teams to ensure ML workloads are production-ready and reliable by design
• Ensure security, compliance, and operational readiness across infrastructure and deployment workflows
• Lead post-incident reviews and drive measurable, long-term reliability improvements

• Deep experience operating Linux infrastructure
  , systems, and networking in production
• Proven impact as an SRE or Dev Ops Engineer supporting complex, distributed systems
• Practical understanding of machine learning systems and neural network workloads in production
• Hands-on experience with Docker and Kubernetes
• Strong scripting skills (
  Bash and/or Python
  )
• Experience with observability tools (Prometheus, Grafana, Datadog, ELK, Open Telemetry)
• CI/CD pipeline ownership experience (Git Hub Actions, ArgoCD, or similar)
• Ability to debug systemic failures across infrastructure, deployments, and workloads
• Clear communicator who works effectively across engineering and data teams

• Experience supporting ML platforms at scale (training and inference)
• AWS or cloud-managed services experience
• Familiarity with data platforms such as Spark, Airflow, or Kafka
• Experience operating in SOC 2 or regulated environments

• Staff-level ownership of mission-critical infrastructure
• Direct influence over how ML and analytics systems run in production
• Engineering culture that values accountability, operational rigor, and impact

📩 Apply or message Strat

ITech to start the conversation.