ML Ops Manager
ML Ops Manager
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Job Description
We are seeking a highly skilledML/MLOps Managerwith 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.
Qualifications :
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 retrainingetc.
Proven experience with at least two MLOps projects deployed using ML Flow or CML.
Strong proficiency in Services like: Bitbucket Git Git actions Jenkins Airflow CML
Expertise in Infrastructure as Code using CloudFormation for dev/test/prod environments.
Solid understanding of MLOps best practices and Data Science principles.
Proficient in Python for scripting and automation.
Experience in converting the Data Science notebooks into production ready deployable components.
Proven experience in building ML pipelines for training inference monitoring.
Experience building and managing Docker images.
Hands-on experience with Git-based version control systems such as Bitbucket GitHub including GitHub Actions for CI/CD pipelines Jenkins or similar tools is a plus.
Remote Work :
No
Employment Type :
Full-time
Employment Type
Full-time
