Lead D&T Machine Learning Engineer

ABOUT GENERAL MILLS

• Development of end-to-end MLOps framework and Machine Learning Pipeline using GCP, Vertex AI, and Software tools
• Serving Pipeline with multiple creation Vertex AI and GCP services. Improve ML pipeline documentation and understandability.
• Automate logging of model usage and predictions provided. Improve logging and diagnostic processes
• Automate monitoring of models both for failures and degradation. Automate monitoring of data sources to identify issues and/or data changes.
• Design and implement dynamic re-training of ML pipelines using event-based or custom logic
• Resource and Infra Monitoring configuration and pipeline development using GCP service.
• Branching strategies and Version Control using GitHub
• ML Pipeline orchestration and configuration using Airflow/Kubeflow.
• Code refactorization & coding best practices implementation as per industry standard
• Implementing MLOps practices on a project and establishing MLOps best practices.
• Lead the investigation and resolution of production issues, perform root cause analysis, and recommend changes to reduce/eliminate re-occurrence of issues.
• Optimize deployment and change control processes for models.
• Create and operationalize quality assurance processes for ML models

• Partners with business stakeholders to design the right deliver value-added insights and intelligent solutions through ML and AI.
• Collaborates with Data Science Leads, ML System Engineering and Platform teams to ensure the models are deployed in a scaled and optimized way. Additionally, ensure support the post-production to ensure model performance degrades are proactively managed.
• Play a lead role in spearheading the development effort of new standards (design patterns, coding practices, orchestration patterns) and drive value and adoption across the Data Science team
• Is considered an expert in the ML Ops and Model management space; brings together business knowledge, architecture, resources, people, and technology to create more effective solutions

• Research and operationalize technology and processes necessary to scale ML Ops
• Recommend model changes to optimize cloud spend.
• Ability to research and recommend MLOps best practices on new technologies, platforms, and services.
• Drive ideation, design, and creation of new ML Architecture patterns in discussion with the Enterprise Architecture team.
• MLOps pipeline improvement plan and suggestion

Communication and Collaboration:

• Knowledge sharing with the broader analytics team and stakeholders.
• Communicate on the on-goings to embrace the remote and geographical culture.
• Ability to communicate the accomplishments, failures, and risks in timely manner.
• Knowledge sharing session with team for specific ML Ops topics. Coach and Mentor junior ML members in the team.
• Foster a collaborative and innovative team environment. Contribute to the overall effort to educate stakeholders on AI practices.
• Closely collaborates with the stakeholders on projects and data science leaders to ensure practices are developed and enhanced to support accelerated analytic development and maintainability.

Embrace a learning mindset:

• Continually invest in one’s knowledge and skillset through formal training, reading, and attending conferences and meetups

• GCP Machine Learning certification
• Understanding of CPG industry
• Exposure to Deep Learning/RL/LLMs
• Prior experience with CPG industry.
• Publications or contributions to the data science and AI community.
• Certifications in AI, machine learning, or related fields.