MLOps Engineer

About The Role The role owns the infrastructure that turns machine learning models into reliable, production systems. This means designing CI/CD pipelines for ML, automating training and deployment workflows, and building the observability layer that catches model drift before it impacts users. The team supports data scientists and ML engineers across the organization - anyone shipping a model depends on the platforms and tooling this role builds. The work spans Kubernetes-based serving infrastructure, experiment tracking, feature stores, and GPU resource orchestration. Key Responsibilities Design and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, Airflow, or Kubeflow, enabling safe automated model releases Build and operate model serving infrastructure on Kubernetes (KServe, Seldon, or Triton) supporting both real-time and batch inference at scale Implement monitoring and alerting for production models: data drift detection, latency SLAs, prediction quality dashboards using tools like Evidently or Prometheus Manage ML platform tooling - experiment tracking (MLflow, W&B), model registries, and feature stores - and drive adoption across ML teams Optimize GPU utilization and cloud costs across training and inference workloads (AWS, GCP, or Azure), including autoscaling and spot instance strategies Automate data pipeline orchestration and ensure reproducibility of training runs through versioned datasets, code, and configs Partner with ML engineers and data scientists to debug production issues, reduce deployment friction, and define platform roadmaps What We Are Looking For 3-6 years of experience in MLOps, DevOps, or ML engineering, with at least 2 years supporting ML systems in production Strong proficiency with Kubernetes, Docker, and infrastructure-as-code (Terraform) in a cloud environment (AWS, GCP, or Azure) Hands-on experience with ML workflow tools: MLflow, Kubeflow, Airflow, or Vertex AI Pipelines Solid Python and/or Go skills; comfort building internal tooling and APIs Experience with model serving frameworks and inference optimization (ONNX, Triton, vLLM, or TensorRT) BS in Computer Science, Engineering, or equivalent practical experience Bonus: Experience with LLM deployment at scale, distributed training infrastructure (Ray, Spark), or FinOps/GPU cost optimization; contributions to open-source MLOps tooling