Job
Description
We are seeking a highly skilled ML/MLOps Manager with an overall experience 8 years with 3 years as ML Engineer particularly in building and managing ML pipelines using MLFlow or CML (Cloudera Machine Learning). The ideal candidate has successfully built and deployed at least two MLOps projects using MLFlow or similar services, with a strong foundation in infrastructure as code and a keen understanding of MLOps best practices. Key Responsibilities Maintain and enhance existing ML pipelines in On Premise with a focus on infrastructure as code. Implement minimal but essential pipeline extensions to support ongoing data science workstreams. Convert the Data Science notebooks into production ready deployable components. Build ML pipelines for training, inference, monitoring. Document infrastructure usage, architecture, and design using tools like Confluence, GitHub Wikis, and system diagrams. Act as the internal infrastructure expert, collaborating with data scientists to guide and support ML model deployments. Research and implement optimization strategies for ML workflows and infrastructure. Work independently and collaboratively with cross-functional teams to support ML product Key Responsibilities Lead the design, development, and management of robust ML pipelines and infrastructure in on-premises or private cloud environments. Define and drive MLOps strategy and best practices for model deployment, monitoring, and lifecycle management. Oversee the implementation and governance of Infrastructure as Code (IaC) using tools like Ansible, Terraform (for private cloud), or Puppet. Manage, mentor, and guide MLOps engineers, fostering a high-performing and collaborative team. Collaborate with cross-functional teams to align MLOps solutions with business and data science objectives. Drive automation and standardization of CI/CD pipelines, model versioning, and container orchestration (e.g., Docker, Kubernetes, OpenShift). Ensure comprehensive documentation of infrastructure, architecture, and operational workflows using tools like Confluence, GitHub Wikis, and system diagrams. Identify and implement optimization opportunities for ML infrastructure performance, cost, and scalability. Stay updated on industry trends and emerging technologies to continuously enhance MLOps capabilities. 8+ years of hands-on MLOps experience with Git Actions, Jenkins or any equivalent tools. Strong knowledge of ML workflow, MLOps concepts like model governance, model monitoring, data drift, retraining etc.
