D&T Machine Learning Engineer II

Overview

D&T Machine Learning Engineer II

Function/Group

Digital and Technology

Location

Mumbai

Shift Timing

Regular

Role Reports to

D&T Manager – ML Engineering

Remote/Hybrid/in-Office

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 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 http://www.generalmills.com

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 https://www.generalmills.co.in

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 an Machine Learning Engineer II to join the Enterprise Data Capabilities Organization. This team builds enterprise-level scalable and sustainable data and model pipelines to serve the analytic needs of business and high-impact problem statements. In this role, you are a critical member of the data science team focused on operationalizing the ML and AI models, which entail model management and monitoring too. The success is to recommend innovative ways to automate the MLOps pipelines on GCP and set standards that would ensure repeated success.

This capability is leveraged to fuel advanced AI solutions, Machine Learning and Deep Learning. It is also responsible for implementing and enhancing the community of practice to determine the best practices, standards, and MLOps frameworks to efficiently delivery enterprise data solutions at General Mills.

This role works in close collaboration with Data Scientists, Data Engineers, Platform Engineers and Tech Expertise to support the analytic consumption needs. Enhances the performance of the models and automates the production pipelines to gain efficiency.

Role Responsibilities

Implement MLOps practices:

• Implementation of end-to-end MLOps framework and Machine Learning Pipeline using GCP, Vertex AI and Software tools on the assigned project(s).
• Assume complete ownership of assigned ML Ops tasks and perform them with high quality adhering to project timelines with minimal external supervision
• Development of feature engineering pipelines including config, ingestion and transformation of data from multiple sources using tools like BigQuery, Dbt & Google cloud storage (GCS), etc.
• Setup Meta Data and Data statistics curation using GCP Bucket and ML Metadata (MLMD)
• Re-Training and Monitoring Pipeline setup with multiple criteria Vertex AI
• Development of Serving Pipeline with Vertex AI and GCP services
• Resource and Infra Monitoring configuration and pipeline development using GCP
• Automated pipeline Development for Continuous Integration (CI)/Continuous Deployment (CD) Continuous Monitoring (CM)/Continuous Training (CT) using GCP-native tool
• ML Pipeline orchestration and configuration using airflow/cloud composer/Kubeflow.
• Code refactorization & coding best practices implementation as per industry standard
• Support the ML models throughout the E2E MLOps lifecycle from development to maintenance.
• Comprehensive documentation to support all stages of ML Ops
  
  

Recommend any changes required to the existing MLOps practices Communication and Collaboration:

• Collaborate with technical teams like Data Science, Data Engineering, and Cloud Platforms, etc.
• Partner with MLOps Domain leads to ensure adoption and implementation of MLOps best practices.
• Knowledge sharing with the broader analytics team and stakeholders is
• Active up-to-date Communication on the in-flight projects to embrace the remote and cross geography
• Align the key priorities and focus
• Ability to communicate accomplishments, failures, and risks in a timely manner.
  
  

• Continually invest in upskilling through formal training, reading, hands-on training, and attending conferences and meetups
  
  

• Document MLOps Process, Development, Architecture & Innovation etc and be instrumental in reviewing the same for other team members.
  
  

• Minimum qualification- Bachelor’s degree (full time)
• Total professional analytics experience required of 6+ Years
• Expertise and at least 3+ years of professional experience in AI and Machine Learning
• Expertise in Data Transformation and Manipulation through Big-Query/SQL
• Professional experience with Vertex AI and GCP Services
• Strong expertise in Python for designing and running ML pipelines
• Airflow/Cloud composer/Kubeflow Experience
• Building and maintaining project specific custom containers
• Strong communication skills both verbal and written including the ability to interact effectively with colleagues of varying technical and non-technical
  
  

• Google Cloud Platform Machine Learning (GCPML) certification
• Understanding of the Consumer-Packaged Goods (CPG) industry
• Strong understanding of Core Machine Learning Algorithms
  
  

• ML Ops framework
• Big Query/SQL
• Python
• Vertex AI and GCP Services
• Docker-Container
• Kubeflow/Kubernetes
• Airflow
• GitHub
• Strong communication skills
• Machine Learning and Deep Learning algorithms
• ML lifecycle stages including Model training, deployment, monitoring etc.
• Agile techniques
• Demonstrates teamworking skills.
• Mentor others and lead best practices.
• Good to have domain knowledge: Consumer Packed Goods industry and data sources.
• Analytic toolset- dbt
• Generative AI
• Agentic AI