India is among the top ten priority markets for General Mills, and hosts our Global Shared Services Centre. This is the Global Shared Services arm of General Mills Inc., which supports its operations worldwide. With over 1,300 employees in Mumbai, the center has capabilities in the areas of Supply Chain, Finance, HR, Digital and Technology, Sales Capabilities, Consumer Insights, ITQ (R&D & Quality), and Enterprise Business Services. Learning and capacity-building is a key ingredient of our success.

Position Title	Lead D&T ML Engineer (Agentic)	Function/Group	Digital and Technology
Location	Mumbai	Shift Timing	Regular
Role Reports to	Sr D&T Manager- Data Science	Remote/Hybrid/in-Office	Remote/Hybrid

ABOUT GENERAL MILLS

We make foodthe world loves: 100 brands. In 100 countries. Across six continents. With iconic brands like Cheerios, Pillsbury, Betty Crocker, Nature Valley, and Häagen-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 Int elligence (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 (Agentic focus) to join our dynamic and innovative Global AI & Automation team. In this role, you are a critical member of the enterprise AI & Agentic group focused on leading efforts in migrating AI-based solutions from concept to production-level operational excellence. The ideal candidate will have expertise in Agentic & AI platforms, AI model / workflow life cycle, Classic & Agentic AI-based workflow / application management including orchestration, deployment, and monitoring, GCP Vertex AI (Gemini Enterprise Agent Platform), and a proven track record of successful AI solution delivery.

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 / AIOps 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.

KEY ACCOUNTABILITIES

Establish and Implement AIOps / Agentic AI practices:

Advanced Agentic Platform Enablement: Lead the technical enablement, integration, and continuous enhancement of advanced agentic AI platforms, ensuring they adhere to stringent performance, scalability, reliability, and security standards. This includes evaluating new agentic-first platforms, tools and frameworks. Expertise in Agentic Deployment capabilities using Google Agent Engine and GKE
Custom Connector & Integration Development: Architect, design, develop, and implement robust custom connectors and integration solutions to seamlessly connect diverse data sources, enterprise applications, and external services with our agentic platforms.
API Design, Testing & Integration: Drive the design, development, and rigorous testing of APIs, ensuring secure, reliable, and efficient integration with both internal and external systems to support agentic workflows.
Agent Orchestration & Workflow Management: Develop, implement, and optimize sophisticated strategies for orchestrating complex agent workflows, managing inter-agent communication, and enhancing decision-making processes within the platform to achieve desired business outcomes.
Monitoring, Observability & Reliability Engineering: Strong understanding and working knowledge to collaborate with other engineering teams to establish, implement and support comprehensive monitoring and observability capabilities for Agentic workflows across multiple platforms. Working knowledge of Datadog is a plus.
ML Systems Engineering: Research, operationalize, and standardize ML tooling and processes, including installation, maintenance, documentation, and best practices.
Generative & Agentic AI Architecture Knowhow: Comprehensive knowledge and working experience (preferred) of setting up an Agentic ecosystem with a cloud platform like Google Cloud Platform (GCP) or Amazon Web Services (AWS).
Platform Optimization & Lifecycle Management: Conduct ongoing optimization, performance tuning, and lifecycle management of agentic platforms and their underlying capabilities, including bug fixes, security patches, and capacity planning.
Drive Agile Agentic POCs: Strong working knowledge and experience to develop & lead solution prototypes from ideation, scoping, implementation, and future road mapping with internal and vendor led teams. Drive agile POC execution with focus on hands-on capability validation on new emerging tools and platforms.
Expert-Level Troubleshooting & Support: Provide advanced technical support and expert-level troubleshooting for complex issues related to agentic platform functionality, integrations, and performance, performing root cause analysis and implementing preventative measures.
Evaluation and Guardrail: Establish evaluation and guardrail patterns for agentic systems, including quality benchma rking, safety controls, prompt and workflow versioning, and human-in-the-loop review where needed.
Technical Documentation Excellence: Create and maintain comprehensive, high-quality technical documentation for platform architecture, custom connectors, API specifications, operational procedures to ensure knowledge transfer and system maintainability.
Governance & Best Practices: Help develop, collaborate, and establish Governance best practices, enterprise standards for Agentic tooling & new agentic solutions.
Security & Compliance: Ensure all platform development and integrations adhere to enterprise security policies, data privacy regulations, and compliance standards.
Embrace learning mindset: Continually invest in your own knowledge and skillset through formal training, reading, and attending conferences and meetups

Lead the execution of AI Solutions Scale:

Partners with business stakeholders to design & deliver the right 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 supporting the post-production phase 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 Generative AI and Agentic AI space brings together business knowledge, architecture, resources, people, and technology to create more effective solutions
Ensure all solutions and integrations adhere to enterprise security policies, data privacy requirements, and compliance standards

Research, Evolve and Publish best practices:

Research and operationalize technology and processes necessary to scale Agentic AI solutions.
Recommend model changes to optimize cloud spend, inference efficiency, latency, throughput, and operational effectiveness
Ability to research and recommend best practices on new technologies, platforms, and services.
Drive ideation, design, and creation of new AI Architecture patterns in discussion with the Enterprise Architecture team.
Identify opportunities to improve agentic frameworks, internal accelerators, and reusable components based on implementation learnings and emerging best practices

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 accomplishments, failures, and risks in timely manner.
Knowledge sharing session with team for specific Agentic AI topics. Coach and Mentor junior ML Engineering 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

MINIMUM QUALIFICATIONS

Advanced degree in a quantitative field (CS, engineering, statistics, math, data science).
Proven technical leadership in a large, complex matrixed organization.
Relevant Agentic AI/Machine Learning experience of 6+ years and overall 12+ years of Industry experience.
Experience in Generative AI / Agentic Solutions, Enterprise scale implementations, and Operations management.
Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities.
Passionate about agile software processes, data-driven development, reliability, and systematic experimentation.
Passion for learning new technologies and solving challenging problems.
Good understanding of CI, CD, TDD, and tools for Enterprise scale Agentic solution deployment.
Strong understand ing of Agentic protocols like MCP, and A2A.
Agile software development experience such as Kanban and Scrum.
Experience in software version control team practices and tools such as GIT and TFS.
Professional experience with Vertex AI and GCP Services.
Working knowledge of Observability solutions like Datadog
Strong proficiency in Python.
Experience contributing to enterprise standards, governance frameworks, or reusable platform accelerators.

PREFERRED QUALIFICATIONS

GCP Generative AI Leader certification
Knowledge of Agentic development frameworks like Google's Agent Development Kit (ADK), LangChain or other similar ones
Hands-on Experience with Multi Agent Orchestration tools like UnifyApps, Zapier, n8n, Lyzr etc.
Understanding of CPG industry
Exposure to SLMs and LLM fine tuning
Prior experience with CPG industry.
Publications or contributions to the data science and AI community.
Certifications in AI, machine learning, or related fields.

Lead Machine Learning Engineer