AI Engineer

The AI Engineer role is responsible for creating, deploying, and maintaining dependable machine learning pipelines and operational frameworks. The position focuses on applying ML Ops and DevOps practices to automate workflows, support model lifecycle management, and help ensure reliable delivery of ML solutions in production.

Responsibilities

• Build and maintain ML pipelines using Python, MLflow, Kubeflow Pipelines, and TFX to automate model training, validation, and deployment.
• Use DevOps practices with Jenkins, GitLab CI/CD, CircleCI, and GitHub Actions to streamline CI/CD for machine learning workflows and help monitor pipeline health.
• Provision and manage scalable cloud resources for ML workloads using Terraform and AWS CloudFormation as infrastructure-as-code tools.
• Implement monitoring and logging with Prometheus, Grafana, ELK Stack, and Fluentd to track model performance, system metrics, and production log analytics.
• Support process compliance through version control and code management using Git, GitHub, GitLab, and Bitbucket.
• 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.

Requirements

• Solid proficiency in ML Ops, including automation of ML pipelines and model lifecycle management.
• Solid understanding of DevOps tooling 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 infrastructure-as-code tools including 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 including Git, GitHub, GitLab, and Bitbucket.
• Solid ability to participate in technical discussions and support process compliance within the team.

Must-Have Skills

• DevOps
• Python
• ML Pipelines

Good-to-Have Skills

• LLM API Integration
• Generative AI Agents

Additional Requirements (Optional but Valuable)

• AWS Certified DevOps Engineer
• Google Professional Machine Learning Enginee

Technology Stack

Python, MLflow, Kubeflow Pipelines, TFX, Jenkins, GitLab CI/CD, CircleCI, GitHub Actions, Terraform, AWS CloudFormation, Prometheus, Grafana, ELK Stack, Fluentd, Git, GitHub, GitLab, Bitbucket, ML Ops

Location and Compensation

• Location: United States (onsite)
• Salary: USD 82,000 - 138,000 per year