New opportunity

Sr. D&T Machine Learning Engineer

General Mills · Mumbai, India

About this role

General Mills, Digital and Technology India, is seeking a Sr. Machine Learning Engineer to join the Enterprise Data Capabilities Organization. This role focuses on building enterprise-level, scalable, and sustainable data and model pipelines to support business-impacting analytic needs. You will be a critical member of the data science team, responsible for operationalizing ML and AI models, including model management and monitoring. The primary goal is to recommend innovative ways to automate MLOps pipelines on GCP and establish standards for repeated success. Key Accountabilities: Design, develop, and maintain end-to-end MLOps pipelines using GCP, Vertex AI, Airflow, Kubeflow, and MLflow. Build and optimize data ingestion, transformation, deployment, monitoring, retraining, and CI/CD/CT pipelines for production ML solutions. Implement MLOps best practices, including version control, coding standards, workflow orchestration, and lifecycle management. Design scalable ML solution architectures and recommend technology stacks. Provide production support, troubleshoot issues, perform root cause analysis, and drive continuous platform improvements. Mentor team members through code reviews and technical guidance. Collaborate with Data Science, Data Engineering, Platform, and Business teams. Research and adopt emerging MLOps technologies and architecture patterns. Minimum Qualifications: Bachelor's degree (full time). Minimum 6 years of professional experience in MLOps E2E framework and 10+ years of total experience. Expertise in data transformation and manipulation through BigQuery/SQL, professional experience with Vertex AI and GCP services, expertise in Python or R, and experience with Airflow/Cloud Composer, Kubernetes/Kubeflow, MLflow, TFX, and Docker. Strong communication skills and passion for agile software processes and data-driven development. Preferred Qualifications: GCP certification, understanding of the CPG industry, and basic understanding of DBT and AutoML concepts.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

MLOpsGCPVertex AIAirflowKubeflowMLflowPythonRBigQuerySQLCloud ComposerKubernetesTFXDockerCI/CDData TransformationAgileDBTAutoML

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Operationalizing End-to-End MLOps Pipelines
The core mandate is to design, develop, and maintain scalable MLOps pipelines on GCP. You must demonstrate expertise in integrating Vertex AI, Airflow, Kubeflow, and MLflow to automate the full lifecycle of ML models, including ingestion, training, deployment, and monitoring.
Establishing Enterprise Standards and Automation
You are expected to recommend innovative ways to automate pipelines and set technical standards for repeated success. This requires a focus on CI/CD/CT (Continuous Integration/Deployment/Training) to ensure production ML solutions are sustainable and reliable.
Cross-Functional Collaboration and Mentorship
As a senior member, you must bridge the gap between Data Science, Data Engineering, and business stakeholders. You will be responsible for mentoring team members through code reviews and providing technical guidance on MLOps best practices.

Likely challenges to think through

Balancing Innovation with Enterprise Stability
You will be tasked with researching and adopting emerging MLOps technologies while maintaining existing production systems. Prepare to discuss how you evaluate new tools for enterprise readiness without disrupting current business-impacting analytic workflows.
Translating Technical Complexity for Diverse Stakeholders
The role requires interacting with both technical teams and non-technical business partners. Prepare to explain how you communicate the value and status of ML initiatives to stakeholders who may not have a deep understanding of MLOps frameworks.
Troubleshooting and Root Cause Analysis in Production
Providing production support for complex ML pipelines involves high pressure. Prepare to share your systematic approach to debugging issues that span across GCP services, containerized environments, and data transformation layers.

Practice your answers

Technical practice

Designing End-to-End MLOps Pipelines on GCP

What to coverDescribe how you would architect a production-grade pipeline using Vertex AI, Airflow, and Kubeflow. Focus on how you ensure scalability, modularity, and seamless integration between data ingestion, model training, and deployment.

Implementing CI/CD/CT for ML Models

What to coverExplain your approach to Continuous Integration, Continuous Deployment, and Continuous Training (CT) in an MLOps environment. Discuss how you handle automated retraining triggers and ensure model performance remains stable in production.

Model Monitoring and Lifecycle Management

What to coverHow do you utilize tools like MLflow or Vertex AI to monitor model drift and performance degradation? Detail your strategy for versioning models and managing the transition from development to production environments.

Data Transformation at Scale with BigQuery

What to coverDiscuss your experience optimizing data transformation workflows using BigQuery and SQL. Explain how you handle large-scale data ingestion and ensure data quality before it reaches the model training stage.

Troubleshooting and Root Cause Analysis in Production

What to coverDescribe a scenario where a production ML pipeline failed. Walk through your systematic approach to identifying the root cause, whether it was a data quality issue, infrastructure failure, or model performance degradation.

Containerization and Orchestration Strategy

What to coverExplain the role of Docker and Kubernetes in your MLOps workflows. Discuss how you manage container images and orchestrate complex multi-step ML workflows to ensure consistency across different environments.

Evaluating Technology Stacks for ML Solutions

What to coverWhen designing a new ML architecture, what criteria do you use to recommend a specific technology stack? Suggestion: Focus on trade-offs between managed services like Vertex AI versus custom implementations using TFX or open-source tools.

Integrating AutoML and DBT into MLOps

What to coverHow would you incorporate AutoML or DBT into an existing enterprise MLOps framework? Discuss the benefits and potential limitations of using these tools for rapid experimentation versus long-term production maintenance.

Behavioural practice

Mentoring and Technical Leadership

What to coverDescribe a time you provided technical guidance or performed a code review for a team member. How did you balance maintaining high coding standards with fostering a collaborative and learning-oriented environment?

Cross-Functional Collaboration

What to coverGive an example of how you have collaborated with non-technical stakeholders or business teams to deliver an ML solution. How did you translate complex technical requirements into business value?

Adapting to Agile Processes

What to coverShare an experience where you had to adapt your development workflow to fit an Agile environment. How did you ensure that MLOps best practices were still upheld during rapid development cycles?

Driving Innovation and Continuous Improvement

What to coverTell us about a time you researched and implemented a new technology or architecture pattern to improve an existing process. What was the impact on the team's efficiency or the platform's reliability?

Sharpen your knowledge

Topics to prepare

End-to-End MLOps Lifecycle Management
Review the full lifecycle of machine learning models from ingestion and training to deployment, monitoring, and automated retraining. Exercise: Map out a production-grade pipeline architecture on GCP that incorporates CI/CD/CT (Continuous Training) principles, specifically identifying where Vertex AI and TFX components integrate to ensure model reliability.
Scalable ML Solution Architecture
Focus on designing architectures that handle enterprise-level data volumes and model complexity. Exercise: Practice drafting a technical proposal for a scalable ML system that addresses potential bottlenecks in data transformation and model serving, ensuring the design aligns with GCP best practices for performance and cost-efficiency.

Skills to revise

GCP Vertex AI and Workflow Orchestration
Gain proficiency in managing ML workflows using Airflow (Cloud Composer) and Kubeflow. Exercise: Create a sample DAG in Airflow that triggers a training job on Vertex AI and logs metadata to MLflow, demonstrating your ability to automate and track model experiments.
Data Transformation with BigQuery and SQL
Master advanced SQL techniques for large-scale data manipulation. Exercise: Optimize a complex SQL query for a large dataset in BigQuery, focusing on partitioning and clustering strategies to reduce latency and costs, which is critical for supporting production-level ML pipelines.
Containerization and CI/CD for ML
Develop expertise in Docker and CI/CD pipelines tailored for ML models. Exercise: Write a Dockerfile for a Python-based model serving application and define a CI/CD pipeline configuration that automates testing and deployment to a Kubernetes environment.

Your preparation checklist

Map MLOps Experience to GCP Architecture
Prepare a technical walkthrough of an end-to-end MLOps pipeline you have built. Focus on how you integrated GCP services like Vertex AI, Airflow, and Kubeflow to handle data ingestion, model training, and CI/CD/CT, specifically highlighting how you ensured scalability and reliability.
Demonstrate Root Cause Analysis Skills
Prepare a case study of a production ML failure you resolved. Detail your troubleshooting process, the metrics you used for monitoring, and how you implemented a permanent fix or architectural improvement to prevent recurrence.
Refine Mentorship and Collaboration Examples
Prepare specific examples of how you have mentored junior engineers through code reviews or technical guidance. Be ready to discuss how you communicate complex MLOps concepts to non-technical stakeholders to drive business alignment.
Formulate Strategic Questions for the Team
Develop thoughtful questions regarding the current state of their MLOps maturity, such as: How does the team currently balance the trade-offs between custom Kubeflow pipelines and AutoML solutions? What are the primary bottlenecks in the current CI/CD/CT lifecycle at General Mills?

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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