Build and operate production-ready ML systems using MLOps and DevOps practices to automate pipelines and strengthen model lifecycle management.

Design, implement, and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment.
Apply DevOps workflows with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for ML processes and support pipeline health monitoring.
Provision and manage scalable cloud infrastructure for ML workloads using Terraform and AWS CloudFormation (infrastructure-as-code).
Implement monitoring and logging with Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and log analytics in production.
Maintain process compliance with team version control and code management using Git, GitHub, GitLab, and Bitbucket.
Join technical discussions and feasibility studies to assess technical alternatives and support architecture best practices for ML Ops solutions.
Prepare and submit status reports to document progress, reduce risks, and support project closure activities.

ML Ops experience, including automation of ML pipelines and model lifecycle management.
DevOps knowledge with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions for workflow automation.
Python skills for scripting, data processing, and ML pipeline development.
Infrastructure-as-code expertise with Terraform and AWS CloudFormation for cloud resource management.
Monitoring and logging capability using Prometheus, Grafana, ELK Stack, and Fluentd.
Comfort working with Git, GitHub, GitLab, and Bitbucket.
Ability to participate in technical discussions and support process compliance within the team.

Location: United States (onsite)

Compensation: USD 82,000 - 138,000 per year

AI Engineer

Must have: DevOps, Python, ML Pipelines
Good to have: LLM API Integration, Generative AI Agents

Job Description