ML-OPS(AI/ML OPS/KUBERNETES) ENGINEER

Job description KeySkills: MLOPS, KUBEFLOW, KUBERNETES, LAKEFS, AI LANDSCAPE, PYTHON, CONTAINERIZATION, DOCKER, CI/CD, DATA VERSIONING, ML PIPELINE ARCHITECTURE, INFRASTRUCTURE DEVELOPMENT, ML WORKFLOWS, DEPLOYMENT & MAINTENANCE Proven experience with MLOps practices and tools (Kubeflow, Kubernetes, LakeFS). Must have experience of the current AI landscape and it s applications. Proficiency in Python and familiarity with containerization tools like Docker. Strong understanding of CI/CD processes, data versioning, and ML pipeline architecture. Excellent problem-solving skills and ability to work in a collaborative team environment. Key Responsibilities: Infrastructure Development: Design and develop scalable ML pipelines integrating LakeFS for data versioning and Kubeflow for orchestrating ML workflows. Should have deep expertise in building & scaling Kubernetes & Docker Containers. Deployment & Maintenance: Deploy, manage, and monitor Kubernetes clusters to efficiently run machine learning workloads. CI/CD Implementation: Build and optimize continuous integration and continuous delivery pipelines for rapid model updates. Collaboration: Work closely with data scientists and software engineers to integrate ML models into production environments and troubleshoot performance issues. Experienced Kubernetes Developer to design, build, and maintain our machine learning infrastructure (Hybrid) Role: Search Engineer Industry Type: Recruitment / Staffing Department: Engineering - Software & QA Employment Type: Full Time, Permanent Role Category: Software Development Education UG: Any Graduate PG: Any Postgraduate Key Skills continuous integrationArchitectureMachine learningInfrastructure developmentInfrastructureDeploymentManagementTroubleshootingMonitoringPython