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HCLTech
MLOps Technical Lead
Career Insights for DevOps Engineer
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Scorecard
Based on North Carolina data
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What they do
A DevOps Engineer works with software engineers and system operators to develop, build and install new technology systems and manage code releases. Participates in strategic project planning; tracks changes in projects and guides project team work as new systems are deployed. Identifies and fixes problems and provides system maintenance.
$125,763 / year median in North Carolina
+15% projected growth
Job Description
MLOps Technical Lead HCLTech - 3.7 Cary, NC Job Details $138,000 a year 3 hours ago Qualifications Tooling Workflow management (operations management method) Infrastructure as Code (IaC) DevOps IT system monitoring Version control systems Production monitoring Cloud automation DevOps automation Cloud monitoring Python MLOps System performance monitoring Full Job Description Cary, North Carolina Job Summary 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. Key Responsibilities 1. Implement and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment processes. 2. Apply DevOps practices with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for machine learning workflows and monitor pipeline health. 3. Utilize infrastructure-as-code tools such as Terraform and AWS CloudFormation to provision and manage scalable cloud resources for ML workloads. 4. Integrate monitoring solutions like Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and log analytics in production environments. 5. Ensure process compliance by using Git, GitHub, GitLab, and Bitbucket for version control and code management within the team. 6. Participate in technical discussions and feasibility studies to evaluate technical alternatives and support architecture best practices for ML Ops solutions. 7. Prepare and submit status reports to highlight progress, minimize risks, and support project closure activities. Skill Requirements 1. Solid Proficiency In Ml Ops, Including Automation Of Ml Pipelines And Model Lifecycle Management. 2. Solid Understanding Of Devops Tools Such As Jenkins, Gitlab Ci/Cd, Circleci, And Github Actions For Workflow Automation. 3. Solid Experience With Python For Scripting, Data Processing, And Ml Pipeline Development. 4. Solid Knowledge Of Infrastructureascode Tools Like Terraform And Aws Cloudformation For Cloud Resource Management. 5. Solid Skills In Monitoring And Logging Tools Including Prometheus, Grafana, Elk Stack, And Fluentd. 6. Solid Familiarity With Version Control Systems Such As Git, Github, Gitlab, And Bitbucket. 7. Solid Ability To Participate In Technical Discussions And Support Process Compliance Within The Team. Other Requirements 1.