MLOps Engineer – DevOps & AI Infrastructure

About the role

We are looking for an experienced MLOps Engineer to design, build and maintain end‑to‑end machine‑learning pipelines and AI infrastructure. The role focuses on integrating DevOps practices with ML workflows to enable reliable, scalable model deployment in cloud environments.

Key responsibilities

• Design, implement and manage complex MLOps deployments using Kubernetes, Docker, Helm, ArgoCD and KubeFlow.
• Develop CI/CD pipelines tailored for machine‑learning projects with Jenkins, Git and DVC.
• Automate infrastructure provisioning and configuration with Terraform, Ansible and cloud‑native services on AWS, Azure or GCP.
• Ensure model versioning, reproducibility and serving using MLFlow, TensorFlow Serving and Seldon.
• Monitor and troubleshoot production systems using Prometheus, Grafana and ELK stacks.
• Collaborate with data scientists, SREs and product teams to translate requirements into reliable pipelines.

Required profile

• 5+ years of hands‑on experience as a DevOps Engineer, SRE Engineer or similar role with a focus on MLOps.
• Strong problem‑solving abilities and experience troubleshooting distributed systems.
• Excellent communication and teamwork skills.
• Relevant certifications such as CKA/CKAD, AWS or Azure are a plus.

Required skills

• Kubernetes, Docker, Helm, Jenkins, ArgoCD, KubeFlow, MLFlow
• Terraform, Ansible, Bash, PowerShell, Python
• Git, DVC
• Cloud platforms: AWS, Azure, GCP
• Linux system administration
• Monitoring tools: Prometheus, Grafana, ELK
• Machine‑learning frameworks: TensorFlow, PyTorch, scikit‑learn
• Model serving: TensorFlow Serving, Seldon