MLOps Engineer

About The Role The role owns the infrastructure and pipelines that turn ML experiments into reliable, production-grade systems. This covers CI/CD for models, distributed training infrastructure, feature stores, model serving, and observability - the plumbing that determines whether an ML organization ships weekly or stalls in notebook limbo.

The position sits at the intersection of platform engineering and machine learning. The MLOps Engineer will work directly with ML engineers and data scientists to make training, deployment, and monitoring fast, reproducible, and boring in the best possible way.

Key Responsibilities

• Design and maintain CI/CD pipelines for ML workflows (GitLab CI, GitHub Actions, or Argo Workflows) covering training jobs, model evaluation gates, and automated deployments
• Build and operate model serving infrastructure using Kubernetes, KServe/Seldon, or cloud-native endpoints (SageMaker, Vertex AI), with autoscaling and canary rollout strategies
• Implement experiment tracking and model registry workflows using MLflow or Weights & Biases, ensuring full lineage from data to deployed artifact
• Build monitoring and observability for production models: drift detection, latency/throughput dashboards, and automated alerting with Grafana, Prometheus, or Datadog
• Optimize training pipelines for cost and speed - distributed training with PyTorch DDP or Ray, GPU utilization tuning, and spot-instance strategies on AWS or GCP
• Automate infrastructure as code with Terraform; own Kubernetes manifests, Helm charts, and environment parity across dev/staging/production
• Partner with data scientists to productionize feature pipelines, leveraging a feature store (Feast, Tecton, or custom) and streaming data with Kafka where appropriate

What We Are Looking For

• 3–6 years of experience in MLOps, platform engineering, or DevOps with significant ML workloads in production
• Strong Python and scripting skills; solid software engineering fundamentals including testing and code review discipline
• Deep hands-on experience with Kubernetes and Docker, including running GPU workloads in production
• Experience with at least one major ML platform: AWS SageMaker, GCP Vertex AI, or Azure ML, plus familiarity with MLflow or W&B
• Practical knowledge of IaC (Terraform) and cloud networking fundamentals on AWS or GCP
• Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience; track record of owning production infrastructure end-to-end
• Bonus: experience with LLM serving stacks (vLLM, Triton, TensorRT-LLM), feature stores (Feast, Tecton), Ray, or exposure to SOC 2 / compliance requirements for ML systems