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 Gen AI solutions.
Key Responsibilities: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.
Requirements: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., Argo CD, Flux) and Infrastructure as Code (Terraform/Ansible). Experience deploying machine learning workloads and familiarity with Gen AI technologies. Strong ownership mindset with excellent documentation and collaboration skills. Ability to work directly with clients and navigate complex environments.
