ML-Ops Engineer

• 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