Senior MLOps Technical Lead

Job Summary

This role is accountable for driving advanced machine learning operations and automation within development projects. The individual leverages expertise in ML Ops, DevOps, and Python to architect, implement, and optimize robust ML pipelines, ensuring efficient model deployment, monitoring, and scalability. They provide advanced proficiency in integrating cloud infrastructure and CI/CD practices, supporting the successful delivery of complex solutions.

Key Responsibilities

1. Implement and optimize ML pipelines using MLflow, Kubeflow Pipelines, and TFX, enabling automated model training, validation, and deployment.
2. Integrate DevOps practices with Python scripting to automate infrastructure provisioning via Terraform, AWS CloudFormation, and Ansible for scalable ML environments.
3. Configure and maintain CI/CD workflows using Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline code integration and deployment for ML projects.
4. Monitor and analyze ML system performance using Prometheus, Grafana, ELK Stack, and Fluentd, ensuring reliability and rapid issue resolution.
5. Apply advanced proficiency in Git, GitHub, GitLab, and Bitbucket for source code management and collaboration within the development team.
6. Participate in technical reviews, contribute to process compliance, and support feasibility studies by evaluating technical alternatives and risks for ML solutions.
7. Prepare and submit project status reports, collaborating with internal stakeholders to define deliverables and minimize escalation risks.

Skill Requirements

1. Advanced Proficiency In Ml Ops, Including Mlflow, Kubeflow Pipelines, Tfx, And Metaflow.
2. Advanced Proficiency In Devops Tools Such As Terraform, Aws Cloudformation, Ansible, Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions.
3. Advanced Proficiency In Python For Automation, Scripting, And Ml Pipeline Development.
4. Advanced Proficiency In Monitoring And Logging Tools: Prometheus, Grafana, Elk Stack, Fluentd.
5. Advanced Proficiency In Version Control Systems: Git, Github, Gitlab, Bitbucket.
6. Solid Understanding Of Cloud Infrastructure And Deployment Strategies.
7. Solid Ability To Troubleshoot, Optimize, And Maintain Ml Environments.

Other Requirements

1. Optional but valuable:
2. AWS Certified Machine Learning � Specialty
3. - Google Professional Machine Learning Enginee