MLOps Engineer

Responsibilities

• Design, implement, and maintain robust CI/CD pipelines for ML models and data workflows
• Automate and monitor the training, validation, deployment, and performance tracking of ML models
• Collaborate with data scientists, ML engineers, and DevOps teams to ensure production-grade ML systems
• Manage and optimize data pipelines using tools like Apache Airflow, Kubeflow, or MLflow
• Set up and maintain model versioning, experiment tracking, and reproducibility systems
• Build infrastructure for scalable model serving using Kubernetes, Docker, and cloud-native services
• Monitor model performance in production and implement re-training and drift-detection mechanisms
• Ensure compliance, security, and governance in all ML workflows

Requirements

• Bachelors or Masters degree in Computer Science, Engineering, or a related field
• 6+ years of experience in software development, DevOps, and/or machine learning infrastructure
• Strong experience with cloud platforms such as AWS, Azure, or GCP
• Proficiency in containerization (Docker) and orchestration (Kubernetes)
• Solid knowledge of CI/CD tools like Jenkins, GitLab CI, or CircleCI
• Experience with ML lifecycle tools like MLflow, Kubeflow, SageMaker, or Vertex AI
• Strong coding skills in Python and experience with ML libraries (scikit-learn, TensorFlow, PyTorch)
• Familiarity with monitoring tools like Prometheus, Grafana, or ELK stack
• Experience with data pipeline tools like Airflow, Luigi, or Prefect

Preferred Qualifications

• Experience with feature stores and metadata management
• Exposure to model interpretability and monitoring tools
• Experience in highly regulated industries such as healthcare or finance
• Certification in cloud platforms like AWS Certified Machine Learning Specialty