Engineering Manager - Machine Learning at Eaton USA

Pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

15 Apr, 26

Salary

0.0

Posted On

15 Jan, 26

Experience

10 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Machine Learning, Artificial Intelligence, Software Development, Team Management, MLOps, DevOps, Cloud Computing, Python, Java, Kubernetes, Docker, Model Monitoring, Hyperparameter Tuning, Version Control, Infrastructure as Code, Problem Solving, Communication

Industry

electrical;Appliances;and Electronics Manufacturing

Description

The Engineering Manager will be responsible to lead our MLOps ML QA and DevOps team in designing, implementing, and optimizing machine learning operational processes and infrastructure. The candidate shall possess a strong background in machine learning, software engineering, and team leadership to drive the successful deployment and management of machine learning models in production environments. This position requires expertise in machine learning lifecycle, software engineering, on premise and cloud infrastructure deployments, as well as strong leadership skills. The Manager will be responsible for: Lead and manage a team of ML Engineers to Validate, develop, deploy, and monitor machine learning models in production. Collaborate with cross-functional teams, including data science, engineering, and operations, 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 to ensure reliability, scalability, and performance. 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, performance degradation, and other issues in production. Provide technical leadership and mentorship to team members, fostering their professional growth and development. Stay current with emerging technologies and industry trends in machine learning engineering, and evaluate their potential impact on our processes and infrastructure. Collaborate with product management to define requirements and priorities for machine learning model deployments and validation, ensuring alignment with business goals and objectives. Implement monitoring and logging solutions to track model performance metrics, resource utilization, and system health, enabling proactive issue detection and resolution. Lead efforts to optimize resource utilization and cost-effectiveness of machine learning infrastructure, including compute resources, storage, and data transfer. Stay abreast of advancements in machine learning technologies, evaluating their applicability and potential impact on our AI Operations strategy and roadmap. Foster a culture of innovation, collaboration, and continuous improvement within the AI Operations team, encouraging experimentation and learning from failures. B.tech / M Tech in Data Science, Computer Science, Electronics or related fields 8 Years + Masters or Bachelor's degree in Computer Science, Engineering, or related field 8+ years of experience in software engineering, data science, data engineering, or related roles, with at least 2 years in a managerial or leadership role. Machine Learning, Artificial Intelligence, Software Development Research and development, Technology strategy, Global Project Management, Team Management, Mentoring, Risk Management. Previous experience in a leadership or management role, with a track record of successfully leading technical teams and delivering high-impact projects. Experience with version control systems (e.g., Git) and collaboration tools (e.g., GitHub, GitLab) for managing code repositories and facilitating team collaboration. Familiarity with infrastructure as code (IaC) tools such as Terraform or CloudFormation for provisioning and managing cloud resources. Knowledge of software development methodologies (e.g., Agile, DevOps) and best practices for building scalable and reliable software systems. Experience with model explainability and bias detection techniques to ensure fairness and transparency in machine learning models. Ability to effectively communicate technical concepts and solutions to non-technical stakeholders, including executives, product managers, and business users. Certification in ML or related areas (e.g., Azure Certified Machine Learning - Specialty, Azure Cloud Professional Machine Learning Engineer) is desirable. Strong proficiency in Python, JAVA and related IDEs Deep understanding of machine learning concepts, algorithms, and frameworks (e.g. TensorFlow, PyTorch, sci-kit-learn). Experience with cloud platforms and services (e.g., Azure, AWS, GCP) for building and deploying machine learning applications. 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 Biase, 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, with the ability to work effectively in cross-functional teams. Recognized as a role model in terms of technical stature Experience with multiple new applications through new product introductions with proven revenue from new products/services. Can influence, resolve, and communicate significant changes to cross-functional teams across the company. Possesses a broad strategic vision of the company's future products and services. Leadership Skills: Responsible for leading the team and helping them to achieve their goals. Able to motivate the team, provide direction, and guide them toward success. Problem-Solving Skills: Able to identify and solve problems quickly, think on your feet, and come up with creative solutions to any issues that arise. Empathy: Being empathetic towards the team members is essential to building a strong team. Able to understand their concerns, identify any issues they may be facing, and help them to overcome any challenges they may be facing. Time Management: Effective time management is essential. Able to prioritize tasks, manage time effectively, and ensure that the team is meeting their deadlines. Communication skills: Excellent written and verbal communication skills, with an ability to write clear and concise material for a range of audiences, including academic, executive, and general business Audiences"

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Responsibilities

The Engineering Manager will lead a team of ML Engineers to develop, deploy, and monitor machine learning models in production. They will collaborate with cross-functional teams to architect end-to-end machine learning pipelines and establish best practices for model management.