MLOps Engineer

About The Role

The role owns the infrastructure and pipelines that turn ML experiments into reliable production systems — CI/CD for models, scalable training infrastructure, feature stores, and low-latency serving environments.

Working at the intersection of data science and platform engineering, this position sits on a team where deployment reliability, model reproducibility, and infrastructure cost efficiency are all first-class concerns, not afterthoughts.

Key Responsibilities

• Design and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, GitLab CI, or Jenkins, with automated testing and validation gates for models and data
• Build and operate model serving infrastructure (KServe, Seldon, SageMaker endpoints, or Triton) supporting low-latency inference at production scale
• Implement experiment tracking and model registry workflows with MLflow, Weights & Biases, or SageMaker Experiments to guarantee reproducibility across environments
• Build monitoring and observability for production ML: data drift detection, latency/throughput dashboards, and automated retraining triggers using tools like Evidently, Prometheus, and Grafana
• Automate data and feature pipelines in partnership with data engineering, leveraging Airflow, Kubeflow, or Dagster orchestration
• Manage Kubernetes-based ML workloads, optimizing GPU utilization, autoscaling policies, and cloud spend across training and inference clusters
• Establish and document MLOps standards — model versioning, rollback procedures, and incident response — and champion them across data science teams

What We Are Looking For

• 3–6 years of experience in MLOps, DevOps, or ML engineering, with at least 2 years deploying and operating ML systems in production
• Strong Python and Bash skills; proficiency with Docker and Kubernetes for containerizing and orchestrating ML workloads
• Hands-on experience with at least one cloud platform (AWS, GCP, or Azure) and its ML-specific services
• Practical experience with ML workflow tooling: MLflow, Kubeflow, Airflow, or equivalent pipelines and registries
• Solid grasp of production ML concerns: model versioning, canary deployments, drift monitoring, and retraining strategies
• Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience
• Bonus: Experience with GPU cost optimization, distributed training (Ray, Dask), infrastructure-as-code (Terraform), or serving LLMs at scale