MLops Engineer

Primary Responsibilities:

• Design and implement MLOps pipelines for continuous integration, deployment, and monitoring of ML models using tools like GitOps, Jenkins, and GitHub Actions
• Manage containerization and orchestration using Docker, Kubernetes, and cloud-based ML platforms such as SageMaker and Vertex AI
• Build and maintain ML orchestration workflows using Kubeflow, Metaflow, MLFlow, and TFX
• Develop infrastructure using Infrastructure-as-Code (IaC) tools like Terraform and AWS CloudFormation
• Monitor model performance and pipeline health using Prometheus, Grafana, and ELK stack
• Implement observable pipelines with metrics tracking, concept drift, and model drift detection
• Automate model retraining, rollback mechanisms, and online/offline validation processes
• Optimize large-scale MLOps pipelines to meet architectural, operational, and organizational demands
• Ensure security and compliance by applying data privacy standards and implementing robust security measures for models and data
• Collaborate with data scientists, software engineers, and product teams to integrate ML solutions into production environments
• Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regard to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so

Qualifications - External

Required Qualifications:

• Bachelor's or Masters degree in Computer Science, Engineering, or a related field
• 6+ years of experience in AIML engineering and research with MLOp
• Proven experience in MLOps, DevOps, or platform engineering roles
• Hands-on experience with Docker, Kubernetes, and cloud ML platforms
• Solid understanding of CI/CD practices for ML workflows
• Proficiency in ML orchestration tools and IaC frameworks
• Familiarity with monitoring tools and performance tracking in production environments
• Knowledge of security and compliance standards in ML systems
• Proven excellent problem-solving, communication, and collaboration skills