MLOps Technical Lead

About The Job:

• Exp – 5 to 9 years
• This role is accountable for building, deploying, and maintaining robust machine learning pipelines and operational frameworks.
• The individual applies solid expertise in ML Ops and DevOps tools to automate workflows, optimize model lifecycle management, and ensure reliable delivery of ML solutions.
• They contribute to project success by implementing best practices, supporting process compliance, and providing technical input within the team.

Essential Job Functions:

• Implement and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment processes.
• Apply DevOps practices with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for machine learning workflows and monitor pipeline health.
• Utilize infrastructure-as-code tools such as Terraform and AWS CloudFormation to provision and manage scalable cloud resources for ML workloads.
• Integrate monitoring solutions like Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and log analytics in production environments.
• Ensure process compliance by using Git, GitHub, GitLab, and Bitbucket for version control and code management within the team.
• Participate in technical discussions and feasibility studies to evaluate technical alternatives and support architecture best practices for ML Ops solutions.
• Prepare and submit status reports to highlight progress, minimize risks, and support project closure activities.

Qualifications:
Skill Requirements

• Solid Proficiency In Ml Ops, Including Automation Of Ml Pipelines And Model Lifecycle Management.
• Solid Understanding Of Devops Tools Such As Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions For Workflow Automation.
• Solid Experience With Python For Scripting, Data Processing, And Ml Pipeline Development.
• Solid Knowledge Of Infrastructureascode Tools Like Terraform And Aws Cloudformation For Cloud Resource Management.
• Solid Skills In Monitoring And Logging Tools Including Prometheus, Grafana, Elk Stack, And Fluentd.
• Solid Familiarity With Version Control Systems Such As Git, Github, Gitlab, And Bitbucket.
• Solid Ability To Participate In Technical Discussions And Support Process Compliance Within The Team.

Other Requirements

• Optional but valuable:
• AWS Certified DevOps Engineer.
• Google Professional Machine Learning Engineer.