MLOps Engineer

Role Overview

We are looking for a skilled MLOps Engineer to design, deploy, and manage scalable machine learning pipelines in production. The role focuses on enabling seamless integration of ML models into enterprise systems with reliability, automation, and governance.

Key Responsibilities

• Design and implement end-to-end ML pipelines from data ingestion to model deployment
• Build and manage CI/CD pipelines for ML models (training, testing, deployment)
• Automate model monitoring, retraining, and performance optimization
• Collaborate with Data Scientists and Data Engineers for productionizing ML models
• Ensure scalability, reliability, and security of ML systems
• Manage model versioning, experiment tracking, and lifecycle management
• Implement best practices for governance, compliance, and reproducibility

Key Skills & Expertise

• Strong programming skills in Python
• Experience with ML frameworks: TensorFlow, PyTorch, Scikit-learn
• Hands-on experience with MLOps tools: MLflow, Kubeflow, Airflow, SageMaker, Azure ML
• Knowledge of CI/CD tools: Jenkins, GitHub Actions, GitLab CI
• Experience with cloud platforms: AWS
• Strong understanding of data pipelines, ETL processes, and distributed systems