MLOps Engineer

**About The Role** The role owns the infrastructure that turns experimental ML code into reliable, scalable production systems \- CI/CD for models, GPU compute orchestration, feature stores, and the observability stack that keeps dozens of models healthy in production. This is a high\-impact role: ML engineers ship faster and data scientists iterate more when the platform underneath them works. The role will build tooling and pipelines that the entire AI organization depends on daily. **Key Responsibilities** * Build and maintain CI/CD pipelines for ML workloads using tools like GitHub Actions, GitLab CI, or Argo Workflows, covering model training, evaluation, and deployment stages * Design and operate Kubernetes\-based model serving infrastructure (KServe, Seldon, Ray Serve, or custom), with autoscaling strategies for both CPU and GPU workloads * Instrument end\-to\-end monitoring for production models: data drift detection, latency/throughput SLOs, and automated rollback triggers using Prometheus, Grafana, and custom alerting * Manage ML experimentation and lineage tooling (MLflow, Weights \& Biases, or equivalent), ensuring reproducibility across training runs, artifacts, and deployments * Optimize cloud costs and utilization across GPU fleets \- spot instance strategies, batch scheduling, and right\-sizing for training and inference workloads on AWS, GCP, or Azure * Build self\-service tooling that lets data scientists deploy models to production without platform team intervention, including templates, golden paths, and internal documentation * Partner with ML engineers and data scientists on architecture reviews, incident response, and postmortems for production ML systems **What We Are Looking For** * 3–7 years of experience in MLOps, DevOps, or infrastructure engineering, with at least 2 years supporting ML systems in production * Strong proficiency in Python and/or Go, plus deep hands\-on experience with Kubernetes in production environments * Experience with major ML platforms and tooling: MLflow, Kubeflow, Vertex AI, SageMaker, or equivalent model lifecycle systems * Solid grasp of infrastructure\-as\-code (Terraform) and cloud networking, IAM, and security best practices on at least one major cloud * Experience building observability for distributed systems: metrics, logging, tracing, and alerting at scale * BS in Computer Science, Engineering, or equivalent practical experience * Bonus: Experience with GPU scheduling and multi\-tenancy (Ray, Volcano, Run:ai), feature store implementations (Feast, Tecton), LLM inference infrastructure (vLLM, Triton), or SOC 2 / compliance\-driven infrastructure work