Eaton Corporation is looking for an Engineering Manager - Machine Learning to lead a team of Engineers focused on deploying and monitoring machine learning models in production. You will architect and implement end-to-end machine learning pipelines and establish best practices for MLOps.

What You'll Do

Lead and manage a team of Engineers to deploy and monitor machine learning models in production.
Work with data engineers for designing data engineering pipelines and performing robust ETL processes.
Collaborate with cross-functional teams to understand business requirements and translate them into scalable ML solutions.
Architect and implement end-to-end machine learning pipelines for model training, testing, deployment, and monitoring.
Establish best practices and standards for model versioning, deployment, and monitoring.
Implement automated processes for model training, hyperparameter tuning, and model evaluation using tools such as Weight and Biases, MLflow, Kubeflow, or similar.
Design and implement infrastructure for scalable and efficient model serving and inference, leveraging technologies such as Kubernetes, Docker, and serverless computing.
Develop and maintain monitoring and alerting systems to detect model drift and performance degradation.
Provide technical leadership and mentorship to team members.
Stay current with emerging technologies and industry trends in machine learning engineering.
Collaborate with product management to define requirements and priorities for machine learning model deployments.
Implement monitoring and logging solutions to track model performance metrics and system health.
Lead efforts to optimize resource utilization and cost-effectiveness of machine learning infrastructure.
Foster a culture of innovation, collaboration, and continuous improvement within the AI Operations team.

What We're Looking For

B.Tech / M.Tech in Computer Science, Electronics or related fields.
8+ years of experience in machine learning and software development.
Experience in research and development, technology strategy, global project management, team management, mentoring, and risk management.

Nice to Have

Masters or Bachelor's degree in Computer Science, Engineering, or related field.
8+ years of experience in software engineering, data engineering, or related roles, with at least 2 years in a managerial or leadership role.
Experience in designing and maintaining scalable data engineering pipelines and performing robust ETL processes.
Previous experience in a leadership or management role, with a track record of successfully leading technical teams.
Experience with version control systems (e.g., Git) and collaboration tools (e.g., GitHub, GitLab).
Familiarity with infrastructure as code (IaC) tools such as Terraform or CloudFormation.
Knowledge of software development methodologies (e.g., Agile, DevOps) and best practices.
Ability to effectively communicate technical concepts to non-technical stakeholders.
Strong proficiency in Python, JAVA and related IDEs.
Awareness of machine learning concepts, algorithms, and frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
Experience with cloud platforms and services (e.g., Azure, AWS, GCP).
Proficiency in containerization technologies (e.g., Docker) and orchestration tools (e.g., Kubernetes).
Hands-on experience with MLOps tools and platforms such as Weight and Biases, MLflow, Kubeflow, TFX, or similar.
Experience in DevOps and DevSecOps tools and practices.
Strong problem-solving skills and ability to troubleshoot complex issues in production environments.
Excellent communication and collaboration skills.