Job
Description
Project description We are seeking a skilled ML Platform Engineer, responsible for automating, deploying, patching, and maintaining our machine learning platform infrastructure. The ideal candidate will have hands-on experience with Cloudera Data Science Workbench (CDSW), Cloudera Data Platform (CDP), Docker, Kubernetes, Python, Ansible, GitLab, and MLOps best practices. Responsibilities Automate deployment and management processes for machine learning platforms using tools such as Ansible and Python. Deploy, monitor, and patch ML platform components, including Cloudera Data Science Workbench (CDSW), Docker containers, and Kubernetes clusters. Ensure high availability and reliability of ML infrastructure through proactive maintenance and regular updates. Develop and maintain comprehensive documentation for platform configurations, processes, and procedures. Troubleshoot and resolve platform issues, ensuring minimal downtime and optimal performance. Implement best practices for security, scalability, and automation within the ML platform ecosystem. Skills Must have DevOps / Platform Engineers+ Cloudera or Azure + Python + ML Hands-on experience with CDSW (Cloudera Data Science Workbench) or similar ML/AI platforms. Strong expertise in containerization and orchestration using Docker and Kubernetes (AKS preferred). Proficiency in Python programming (enterprise-level applications, automation, and scripting). Experience with Ansible for infrastructure as code (IaC), deployment automation, and configuration management. Strong knowledge of Unix/Linux systems (administration, troubleshooting, performance tuning). Practical experience with GitLab for source control and CI/CD pipeline automation. Deep understanding of MLOps principles and best practices (deployment, monitoring, lifecycle management of ML workloads). Experience in designing, developing, and maintaining distributed systems and services. Proven ability in patching, updating, and maintaining platform infrastructure. Nice to have Previous banking domain Exp Familiarity with Cloudera CDP ecosystem (beyond CDSW). Knowledge of monitoring & observability tools (Prometheus, Grafana, ELK). Exposure to Airflow, MLflow, or Kubeflow for workflow and ML lifecycle orchestration. Cloud platform experience with Azure (AKS, networking, storage, monitoring)
