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# MLOps Technical Lead

- **Company:** HCL America Inc.
- **Location:** Virginia Beach, VA, United States
- **Experience:** Expert
- **Salary:** $139,000.0
- **Contract:** Permanent contract
- **Skills:** Cloud Computing, Continuous Integration, DevOps, Github, Python (Programming Language), Log Analysis, Machine Learning, Prometheus, Circleci, Data Logging, Data Processing, Fluentd, Grafana, Gitlab, Git, Cloudformation, Gitlab-ci, Kubernetes, Infrastructure Automation Frameworks, Bitbucket, Machine Learning Operations, Terraform, Software Version Control, Data Pipelines, Elk Stack, Jenkins
- **Published:** July 31, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=218efd1b66f865e5

## About the Role

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., 3. - Google Professional Machine Learning Enginee

## Description

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., 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.


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