Job Description

:
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)

Skills Required