MLOps Engineer

About The Role The role sits at the intersection of machine learning and infrastructure: making sure models that data scientists build actually ship, scale, and stay healthy in production.

You will build and own CI/CD pipelines, serving infrastructure, and observability systems for ML workloads — the backbone that turns experiments into reliable, monitored products.

Key Responsibilities

• Design and maintain end-to-end ML pipelines (training, validation, deployment) using tools like Kubeflow, MLflow, Airflow, or Vertex AI Pipelines
• Build CI/CD workflows for model and code releases using GitHub Actions, GitLab CI, or Jenkins, with automated testing and staged rollout strategies
• Deploy and scale model serving infrastructure using Kubernetes, Docker, and frameworks like KServe, Seldon, or Triton Inference Server
• Implement model monitoring for data drift, latency, and performance regression using tools like Evidently, Prometheus, and Grafana, with automated alerting and rollback triggers
• Manage feature stores and data versioning systems (Feast, DVC, Delta Lake) to ensure consistency between training and serving environments
• Optimize infrastructure costs and GPU utilization across training and inference workloads
• Partner with data scientists and ML engineers to productionize new models, providing clear feedback loops on what breaks and why

What We Are Looking For

• 3–7 years of experience in DevOps, platform engineering, or MLOps, with at least 2 years supporting ML systems in production
• Strong hands-on experience with Kubernetes and Docker, including deploying stateful and GPU-backed workloads
• Proficiency in Python and Bash; ability to build tooling and automate operational workflows, not just maintain them
• Experience with at least one major cloud platform (AWS, GCP, or Azure) and its ML/managed services
• Working knowledge of ML fundamentals — enough to reason about model versioning, evaluation, and drift without needing a data scientist translate
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience