MLOps​/Platform Engineer

We are looking for an MLOps / Platform Engineer
. In this hands‑on role, you'll design and implement secure, scalable deployment pipelines in on‑premises and private cloud environments, directly supporting machine learning workloads and GenAI solutions.

• Operate and manage Kubernetes clusters in production, designing deployments for maximum efficiency and reliability.
• Develop and maintain CI/CD pipelines using tools like Jenkins, ensuring seamless automation from build to deployment.
• Implement observability practices, including logging and metrics, to quickly identify and resolve issues.
• Deploy ML models and services, ensuring high performance and scalability while optimizing inference processes.
• Work closely with AI engineers to transition prototypes into production‑grade deployments and maintain thorough documentation.

• 3–8 years in Platform Engineering, Dev Ops, or Site Reliability Engineering (SRE).
• At least 2 years of experience with Kubernetes in a production environment.
• Proficient in Docker, CI/CD practices, and Linux.
• Knowledge of Git Ops tools (e.g., ArgoCD, Flux) and Infrastructure as Code (Terraform/Ansible).
• Experience deploying machine learning workloads and familiarity with GenAI technologies.
• Strong ownership mindset with excellent documentation and collaboration skills.
• Ability to work directly with clients and navigate complex environments.