MLOps Engineer

Tehran, Iran | Full-time | Salary: Negotiable (Base + Bonus)

About the Role
Bridge GPU/CPU health automation and ML lifecycle management. You’ll automate node health checks and build robust Kubeflow pipelines, ensuring a high-performance environment for AI engineers.

Key Responsibilities

• GPU/CPU Health Automation (Priority #1): Automate health checks during node provisioning (link stability, ECC, PCIe, thermal) and store results in a Ground Truth Library; collaborate with GPU Performance Engineering on thresholds.
• Build and maintain end-to-end ML pipelines in Kubeflow (deployment, monitoring).
• Manage Kubernetes clusters with Docker in production.
• Automate CI/CD via GitHub Actions (or GitLab CI/Jenkins).
• Write Python/Bash scripts for Linux systems and infrastructure tasks.
• Implement monitoring/alerting with Prometheus & Grafana (ELK stack is a plus).
• Troubleshoot across dev/staging/production; document processes, runbooks, and postmortems.
• Ensure security, compliance, and governance in cloud/on-prem environments.

Required Skills & Qualifications

• 2+ years in MLOps, DevOps, SRE, or infrastructure roles.
• Strong Python and Linux skills.
• Hands-on Kubernetes & Docker in production.
• Experience with CI/CD (GitHub Actions, GitLab CI, or Jenkins).
• Understanding of deep learning pipelines & frameworks (TensorFlow/PyTorch).
• Prometheus & Grafana experience (ELK familiarity is a plus).
• Basics of networking, security, system administration, and databases.
• Hardware-level diagnostic concepts (ECC, PCIe errors, thermal, link stability).
• Cloud fundamentals (AWS, GCP, or Azure).

Personal Attributes & Soft Skills

• Detail-oriented
• Problem-solving & logical thinking
• Curious & inquisitive
• Teamwork & collaboration
• Patience & perseverance
• Quick learner, flexible
• Responsibility & discipline
• Creative & innovative thinking
• Ownership mindset for reliability and outcomes
• Resilience under pressure; R&D experimentation mindset

Nice-to-Have

• SRE practices (SLIs/SLOs, incident response)
• IaC tools (Terraform, Ansible)
• Distributed systems & microservices architecture
• Prior GPU workload or hardware validation exposure
• Kubeflow/MLflow advanced experience
• Relevant certifications (CKA/CKAD, cloud certs)

Benefits

• R&D-driven environment with cutting-edge GPU technologies
• Competitive salary + performance bonus
• Continuous learning, skill-building, and career growth
• Shared individual and organizational development goals