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General Mills

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4.2

based on 765 Reviews

Video summary

74 General Mills Jobs

Lead D&T Machine Learning Engineer

General Mills, Inc.

4.2

based on 765 Reviews

6-10 years

Mumbai

1 vacancy

Lead D&T Machine Learning Engineer

General Mills

posted 3mon ago

Job Role Insights

Flexible timing

Key skills for the job

Data Science Medical Coding SAP CO Data Analytics Python SQL

+ 3 more

Job Description

The Digital and Technology team at General Mills stands as the largest and foremost unit, dedicated to exploring the latest trends and innovations in technology while leading the adoption of cutting-edge technologies across the organization. Collaborating closely with global business teams, the focus is on understanding business models and identifying opportunities to leverage technology for increased efficiency and disruption. The teams expertise spans a wide range of areas, including AI/ML, Data Science, IoT, NLP, Cloud, Infrastructure, RPA and Automation, Digital Transformation, Cyber Security, Blockchain, SAP S4 HANA and Enterprise Architecture. for the company. Employees working on significant technology projects are recognized as Digital Transformation change agents.

The team places a strong emphasis on service partnerships and employee engagement with a commitment to advancing equity and supporting communities. In fostering an inclusive culture, the team values individuals passionate about learning and growing with technology, exemplified by the Work with Heartphilosophy, emphasizing results over facetime. Those intrigued by the prospect of contributing to the digital transformation journey of a Fortune 500 company are encouraged to explore more details about the function through the provided Link

Purpose of the role

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.

KEY ACCOUNTABILITIES

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

MINIMUM 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 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.