▷ Apply Now Lead D&t Machine Learning Engineer

Position Title Lead ML Engineer Function Group Digital and Technology Location Mumbai Shift Timing Regular Role Reports to Sr Manager- Data Science Remote Hybrid in-Office Remote Hybrid ABOUT GENERAL MILLS We make food the world loves 100 brands In 100 countries Across six continents With iconic brands like Cheerios Pillsbury Betty Crocker Nature Valley and Haagen-Dazs we ve been serving up food the world loves for 155 years and counting Each of our brands has a unique story to tell How we make our food is as important as the food we make Our values are baked into our legacy and continue to accelerate us into the future as an innovative force for good General Mills was founded in 1866 when Cadwallader Washburn boldly bought the largest flour mill west of the Mississippi That pioneering spirit lives on today through our leadership team who upholds a vision of relentless innovation while being a force for good For more details check out General Mills India Center GIC is our global capability center in Mumbai that works as an extension of our global organization delivering business value service excellence and growth while standing for good for our planet and people With our team of 1800 professionals we deliver superior value across the areas of Supply chain SC Digital Technology D T Innovation Technology Quality ITQ Consumer and Market Intelligence CMI Sales Strategy Intelligence SSI Global Shared Services GSS Finance Shared Services FSS and Human Resources Shared Services HRSS For more details check out We advocate for advancing equity and inclusion to create more equitable workplaces and a better tomorrow JOB OVERVIEW General Mills Digital and Technology India is seeking a Lead ML Engineer to join our dynamic and innovative Global Data Science team In this role you are a critical member of the data science group focused on leading efforts in migrating ML-based solutions from concept to production-level operational excellence You will lead initiatives building scalable resilient and automated solutions in GCP Google Cloud Platform to ensure that models deliver on organizational objectives You will professionally engineer solutions considering notions of risk and FMEA failure modes and effects analysis The ideal candidate will have expertise in AI platforms ML model development life cycle model management including orchestration deployment and monitoring GCP Vertex AI and a proven track record of successful AI solution delivery The ML Engineering capability is leveraged to fuel advanced AI ML solutions driving decision-making for critical enterprise needs It is also responsible for implementing and enhancing the community of practice to determine the best practices standards and MLOps frameworks to efficiently deliver enterprise data solutions at General Mills This role works in close collaboration with Data Scientists Data Engineers Architects and other teams to support the analytic consumption needs Enhances the performance of the models and automates the production pipelines to gain efficiency Roles Responsibilities Establish and Implement MLOps practices 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 Lead the execution of ML Solutions Scale 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 Evolve and Publish best practices 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 Good to have skills 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 Technical Skill proficiency expectations Expert Level Intermediate Level Basic Level ML Ops framework Big Query SQL Python R Vertex AI and GCP Services Docker-Container ML Orchestrator Kubeflow Airflow GitHub Strong communication skills Machine Learning and Deep Learning algorithms Agile techniques Demonstrates teamwork skills Mentor others and lead best practices Understanding of ML Architecture Consumer Packaged Goods domain knowledge Large Language Models and deployment architecture Graph database Tools like Neptune AI ML Flow Feature Store GCP Vertex Feature Store Feast etc