MLOps Engineer

ABOUT THIS FEATURED OPPORTUNITY

The MLOps Engineer will join the Channel Sales and Operations team and build and maintain infrastructure for deploying, monitoring, and scaling ML models, while enabling fast experimentation through CI/CD pipelines and efficient tooling. This role sits at the intersection of DevOps, ML engineering, and backend development, with a focus on automation, reliability, and performance.

THE OPPORTUNITY FOR YOU

• Design, automate, and maintain infrastructure-as-code with Terraform across cloud environments (GCP preferred).
• Manage containerized workloads with Kubernetes, Helm charts, and service meshes (Istio for advanced networking).
• Build and maintain scalable CI/CD pipelines for ML model training, validation, deployment, and monitoring.
• Develop and optimize FastAPI services for inference APIs and internal tooling.
• Implement deployment strategies (blue/green, canary, A/B testing) for ML models in production.
• Collaborate with data scientists to take models from experimentation to reliable production deployment.
• Utilize PromptQL and other observability tools to track model performance, monitor dashboards, and ensure reliability.
• Ensure infrastructure scaling, GPU/TPU utilization, and cost optimization in ML workloads.
• Troubleshoot and optimize distributed systems for high availability and performance.

KEY SUCCESS FACTORS

• 3+ years of DevOps and cloud infrastructure experience
  • Strong experience with Terraform (commands like init, plan, apply, destroy) for IaC
  • Hands-on with Kubernetes (kubectl get pods, kubectl scale deployment, kubectl describe, etc.) for scaling and monitoring workloads
  • Experience creating and deploying Helm charts from Docker images
• Proficiency in Python with ability to write clean, testable, and scalable code
  • Strong understanding of FastAPI and asynchronous programming (I/O bound vs CPU bound workloads)
• Machine learning and model deployment experience
  • Understanding of the ML lifecycle : training → validation → deployment → monitoring
  • Knowledge of monitoring, logging, and alerting for ML systems