Senior ML Ops and Automation Engineer

As an ML Ops and Automation Engineer, you will be at the forefront of bridging the gap between machine learning (ML) development and production deployment, ensuring smooth and efficient operations of ML systems. Your primary focus will be on designing, implementing, and maintaining automated pipelines for model training, deployment, monitoring, and scaling, with the aim of optimizing performance, reliability, and scalability of ML applications. You will collaborate closely with cross-functional teams including data scientists, software engineers, and DevOps to streamline the ML lifecycle and drive innovation in machine learning infrastructure.

Key Responsibilities

– Design and Implement ML Pipelines: Develop end-to-end automation pipelines for ML model training, validation, deployment, and monitoring, integrating with CI/CD systems and version control tools.

-Infrastructure Orchestration: Architect and manage scalable, reliable infrastructure for ML workloads, leveraging cloud services (e.g., AWS, Azure, GCP) and containerization technologies (e.g., Docker, Kubernetes) to ensure high availability and performance.

-Model Versioning and Experiment Tracking: Establish frameworks for versioning ML models and tracking experiment results, enabling reproducibility and collaboration among data scientists and ML engineers.

-Continuous Integration/Continuous Deployment (CI/CD): Implement CI/CD pipelines for automated testing, deployment, and rollback of ML models, ensuring rapid iteration and deployment cycles. (Terraform, AWS cloudformation) -Monitoring and Alerting: Set up robust monitoring and alerting systems to track the performance, health, and drift of deployed ML models in real-time, proactively identifying and addressing issues.

-Optimization and Scaling: Optimize ML workflows for efficiency and cost-effectiveness, and scale infrastructure to accommodate growing data volumes and user loads.

-Security and Compliance: Implement best practices for data security, privacy, and compliance (e.g., GDPR, HIPAA), and ensure adherence to regulatory requirements in ML workflows and deployments.