ML Infrastructure / MLOps Engineer

**About Aivar Innovations** --------------------------- Aivar is an **AI\-first technology partner** where cutting\-edge technology meets industry expertise to supercharge your projects.**Team: Accelerators** ---------------------- **Experience:** 3–7 years \| MLOps/ML platform \+ Kubernetes **Technical Focus:** Own the JARK\-Stack integration on EKS: Ray \+ KubeRay for distributed compute, Kubeflow Pipelines for workflow orchestration, MLflow for experiment tracking, JupyterHub for development, and advanced job schedulers (Kueue, Volcano, Argo) for batch training. A bridge between data scientists and the platform.**Key Responsibilities:** ------------------------- * Deploy and optimise Ray \+ KubeRay for distributed data processing and model training across GPU clusters. * Build Kubeflow Pipelines for reproducible ML workflows — data prep, training, evaluation, deployment with lineage tracking. * Configure MLflow for centralised experiment tracking and model registry across teams. * Implement advanced job scheduling — queue management, priority, preemption, gang scheduling via Kueue/Volcano. * Build model CI/CD — automated training, evaluation, validation, and canary/blue\-green deployment to inference endpoints. * Create self\-service tooling for data scientists — cluster provisioning, GPU allocation, experiment templates. * Monitor ML workload performance — GPU utilisation, training throughput, data pipeline efficiency. **Must\-Have Technical Skills:** -------------------------------- * ML infrastructure / MLOps / ML platform engineering (3\+ years). * Kubernetes (EKS preferred) — deployments, PVs, RBAC, resource management. * At least two of: Ray/KubeRay, Kubeflow, MLflow, Airflow, Argo Workflows. * Distributed training — PyTorch DDP, Horovod, DeepSpeed, or Ray Train. * Model serving — KServe, Seldon, or custom FastAPI serving. * GPU scheduling and resource management on Kubernetes. * Strong Python engineering — tools and automation, not just notebooks. **Core Tech Stack:** -------------------- Ray/KubeRay, Kubeflow Pipelines, MLflow, JupyterHub, Argo Workflows, Kueue/Volcano, PyTorch/DeepSpeed, KServe, Helm, AWS (EKS, S3, EFS, ECR), Prometheus/Grafana. Note: This is a third party job (Aggregated by careeruplift.ai). Shortlisting and Final hiring decision & process is handled by the company.