You need to have a proven experience in followong:

• Infrastructure & IaC: Minimum 250 person-days managing server/cloud infrastructure (Public/Private), IaC tools (Terraform, Bicep, etc.), Docker, and Kubernetes
• CI/CD: Minimum 300 person-days designing and maintaining CI/CD solutions in production environments
• MLOps / ALM: Minimum 200 person-days deploying and utilizing MLflow, Kubeflow, ClearML, or similar platforms
• Incident Management: Minimum 150 person-days in root cause analysis and stabilizing critical systems
• Strong Linux systems engineering background (RHEL/Rocky/SLES)
• Proficiency in Python and Bash for automation
• Experience with relational databases (PostgreSQL), NFS, and Object stores (S3-compatible)
• Experience with Data Analytics & Data Analysis (working with Databricks)
• Strong analytical mindset with an implementation-oriented approach
• Ability to translate business requirements into scalable technical solutions
• Excellent cross-functional collaboration (with Data Scientists, Developers, and IT operations)

Will be a plus:

• Deep experience with HPC schedulers (PBS Professional, Torque, Slurm) and building integrations (hooks, prolog/epilog scripts)
• Experience bridging traditional HPC schedulers with modern cloud-native platforms (Kubernetes, MLOps stacks) and configuring dynamic scaling (cloud bursting)
• Scripting abilities in Go or Rust
• Proficiency in SQL and PowerShell
• Familiarity with MPI workloads (OpenMPI, MPICH) and GPU scheduling (NVIDIA stack, MIG/MPS)
• Experience with parallel file systems (Lustre strongly preferred)
• Configuration management experience (Ansible, Puppet, or similar)

Key Responsibilities:

• Design, deploy, and support resilient infrastructure for machine learning platforms and data pipelines using Python and SQL
• Implement Application Lifecycle Management (ALM) for machine learning, automating training, versioning, and deployment processes (MLflow, Kubeflow, ClearML, or similar enterprise ML platforms)
• Ensure reliability, scalability, and high availability of the MLOps infrastructure and backend services
• Design and manage distributed compute environments (bare metal, VM, private/public cloud)
• Containerize ML services and applications using Docker and Kubernetes, orchestrating smooth production rollouts
• Automate infrastructure provisioning, cluster lifecycle, and configuration using Infrastructure as Code (Terraform, Bicep, ARM, etc.)
• Build, integrate, and maintain robust CI/CD pipelines (GitLab CI, GitHub Actions, Jenkins)
• Implement comprehensive observability (logging, metrics, dashboards) for overall cluster health
• Diagnose bottlenecks, resolve node/network failures, and conduct Root Cause Analysis (RCA) as part of proactive incident management