Key Responsibilities

• Model Deployment & Automation
  • Design and manage CI/CD pipelines for ML models using tools like MLflow, Kubeflow, or SageMaker.
  • Automate model training, validation, and deployment workflows.
• Infrastructure & Scalability
  • Architect and maintain scalable ML infrastructure on cloud platforms (AWS, Azure, GCP).
  • Optimize resource usage and model performance in production environments.
  • Support distributed training and real-time inference systems.
• Monitoring & Governance
  • Implement monitoring systems for model drift, performance, and data integrity.
  • Ensure compliance with data governance, privacy, and security standards.
  • Establish observability and reliability practices for ML systems (SLOs, alerting).
• Collaboration & Leadership
  • Work closely with data scientists, software engineers, and DevOps teams to integrate ML solutions.
  • Mentor junior ML engineers and contribute to technical leadership across projects.
• Tooling & Frameworks
  • Develop reusable components and libraries for ML Ops workflows.
  • Evaluate and integrate new tools and technologies to improve ML lifecycle management.

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, or related field.
• 8-10 years of experience in software engineering, data science, or ML Ops.
• Strong proficiency in Python, Docker, Kubernetes, and cloud-native ML tools.
• Experience with ML lifecycle platforms (e.g., MLflow, TFX, Airflow).
• Deep understanding of model versioning, reproducibility, and deployment strategies.