Role Description 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. - 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. - Ensure support post-production to manage model performance degradation proactively. - 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. - 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 a 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. - Embrace a learning mindset: - Continually invest in one’s knowledge and skillset through formal training, reading, and attending conferences and meetups. Qualifications - Full-time graduate from an accredited University. - Advanced degree in a quantitative field (CS, engineering, statistics, math, data science). - Proven technical leadership in a large, complex matrixed organization. - Relevant Machine Learning experience of 6+ years and overall 12+ years of Industry experience. - Experience in supervised ML algorithms, optimization, and performance tuning. - Track record of producing machine learning models and production infrastructure at scale. - 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 such as Jenkins. - Strong understanding of orchestration frameworks such as Airflow/Kubeflow/MLFlow. - Agile software development experience such as Kanban and Scrum. - Experience in software version control team practices and tools such as GIT and TFS. - Expertise in Data Transformation and Manipulation through Big-Query/SQL. - Professional experience with Vertex AI and GCP Services. - Strong proficiency in Python. Preferred Qualifications - 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.

• Own end-to-end delivery of AI features across model, backend, APIs, UI integration, deployment, monitoring, and iteration in production • Solve complex challenges with AI/ML: Design, develop new AI-powered products that deliver the product roadmap, including agentic AI solutions that orchestrate LLMs, tools, and workflows to solve multi-step problems autonomously. • Implement ML lifecycle - from data engineering and model development to cloud-based deployment, integrations and operationalisation. Incl. MLOps • Productionise full-stack AI/ML solutions: Translate emerging techs like GenAI& agenticAI architectures into innovative, practical solutions that transform customer experiences. • Align with Product Strategy: Create proof of concepts at high cadence to demonstrate/validate potential solutions as per our product strategy. • Optimise Model and system performance: Fine-tune, optimise training and inference performances, including latency, cost, and reliability trade-offs in agent-based and LLM-driven systems. • Wider collaboration: Partner with cross-functional teams to demonstrate and validate the impact of ML innovations before introducing them into the product ecosystem. • Research Savvy: Staying up-to-date with SOTA and industry trends in AI/ML, with a strong awareness of advances in agentic systems, autonomous workflows, and multi-agent architectures.

• Enable product teams: teach, coach, and guide them on data and ML best practices • Lead by example: do complex data analysis and ML modeling, architecture, and implementation work as needed to accelerate teams while mentoring more junior data/ML folks. • Explore and analyze data using Python, pandas, and PySpark • Use matrix factorization, clustering, dimensionality reduction, and related techniques to understand and prepare data for modeling, and to identify and label latent factors (e.g., user behavior patterns, content/topic clusters, expertise dimensions). • Create, tune, and productionize ML models for categorization / classification, recommendations and similarity, and other prediction or ranking tasks that power product features. • Design and implement AI-driven ingest flows that turn unstructured inputs (tickets, emails, forms, messages, logs, etc.) into well-structured data that models and downstream systems can use. • Build workflows where AI can auto-fill or suggest key fields and metadata. • Work closely with engineers to integrate models and workflows into production systems with proper monitoring, fallbacks, and guardrails.

Role Description We are looking for a highly capable Senior MLOps Engineer with a strong Software Engineering and DevOps background. As a Senior MLOps Engineer, you will be embedded and supporting a revenue generation or cost optimization project, ensuring its success in production by: - Improving the code - Creating automated CI/CD testing - Developing frameworks that can be reused for other similar projects - Build, maintain, and document machine learning frameworks (python packages) used across multiple projects - Support a project team with Data Scientists, Business Stakeholders, Analysts, and Data Engineers - Develop reusable feature stores for rules-based and AI/ML models - Implement monitoring capabilities for model performance and effectiveness in production - Automate CI/CD testing and deployments incorporating MLOps best practices Qualifications - Bachelor's degree in software engineering, computer science, data science, mathematics, or a related field - 5+ years of overall experience in Data Analytics - 3+ years of experience with ML Engineering and/or ML Ops (Up to 2 years of Software Engineering or Data Engineering experience can also count towards this requirement) - Sharp critical thinking skills and ability to learn and question complex processes and solutions - Experience building scalable machine learning systems and data-driven products working with cross-functional teams - Experience creating python packages - Well-developed software engineering skills, including use of proper development, QA, and production environments, object-oriented programming, version control, and knowledge of multiple programming languages - Proficiency in Python and experience with common data analytics packages (e.g. Numpy, Pandas, Sklearn, PySpark) - Proficiency in SQL Company Description Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2024 global revenues of €22.1 billion.