MLOps Engineer

Scale.jobs is seeking an MLOps Engineer to bridge the gap between machine learning model development and production-grade software engineering. The role involves designing CI/CD pipelines, deploying models on Kubernetes, and building monitoring systems. Candidates should have 3-6 years of experience with Python, Docker, Kubernetes, and cloud infrastructure.

• Design and implement scalable CI/CD pipelines for ML models using GitOps principles (GitLab CI, GitHub Actions, Argo Workflows)
• Deploy and orchestrate ML workloads on Kubernetes using Kubeflow, MLflow, or Seldon Core
• Build and maintain centralized feature stores (e.g., Feast, Tecton)
• Establish automated monitoring, logging, and alerting for models using Prometheus and Grafana
• Optimize model serving infrastructure for low latency using Triton Inference Server, TorchServe, or TF Serving
• Collaborate with security teams on access controls and model lineage tracking

• 3–6 years experience in MLOps, DevOps, or Software Engineering with ML infrastructure focus
• Expert-level Python and deep familiarity with Docker and Kubernetes
• Hands-on experience with cloud infrastructure (AWS, GCP, or Azure) and Terraform
• Proven track record with ML pipeline orchestration (e.g., Apache Airflow, Prefect, Argo)