Skip to the page

Lead Machine Learning Engineer (MLops)

GenmillsWorkdayPowai, Mumbai, MH
Posted 3 AugFound 31 OctSeen 8 hours ago

Fit

The keywords the posting names, against what your resume says.

Add your resume to see which of its keywords you already have, and get a resume tailored to this posting. 10 a month, free.

The Posting

COMPANY OVERVIEW

We exist to make food the world loves. But we do more than that. Our company is a place that prioritizes being a force for good, a place to expand learning, explore new perspectives and reimagine new possibilities, every day. We look for people who want to bring their best — bold thinkers with big hearts who challenge one another and grow together. Because becoming the undisputed leader in food means surrounding ourselves with people who are hungry for what’s next.​




OVERVIEW

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.

KEY ACCOUNTABILITIES

  • Design, develop, and implement end-to-end MLOps pipelines using GCP, Vertex AI, Kubeflow, and Airflow.
  • Automate model deployment, monitoring, retraining, logging, and ML pipeline orchestration.
  • Establish and drive MLOps best practices, including version control, CI/CD, coding standards, and quality assurance.
  • Optimize ML model performance, deployment processes, cloud infrastructure, and operational efficiency.
  • Lead production support, troubleshoot issues, perform root cause analysis, and implement preventive solutions.
  • Partner with Data Science, Engineering, and Business teams to deploy scalable, production-ready ML solutions.
  • Drive ML architecture standards, reusable design patterns, and platform improvements across the organization.
  • Research and adopt emerging MLOps technologies and best practices to enhance scalability and reduce cloud costs.
  • Mentor team members, promote knowledge sharing, and foster a collaborative engineering culture.
  • Continuously enhance technical expertise through learning and adoption of new technologies.

MINIMUM QUALIFICATIONS

Education: Minimum Bachelor's degree, Advanced degree in a quantitative field (CS, engineering, statistics, math, data science).

Experience: Relevant Machine Learning experience of 7+ years in MLOps and overall 12+ years of Industry experience

Technical Skills:

  • Strong proficiency in Python, SQL/BigQuery, and Vertex AI on GCP.
  • Experience building and deploying production-scale ML models with performance optimization.
  • Hands-on experience with Airflow, Kubeflow, MLflow, and MLOps orchestration.
  • Knowledge of CI/CD, TDD, Jenkins, and version control tools such as Git.
  • Experience working in Agile (Scrum/Kanban) development environments.
  • Strong understanding of supervised ML algorithms, data transformation, and feature engineering.
  • Passion for learning new technologies and solving complex engineering problems.

Soft Skills: Strong verbal and written communication skills including the ability to interact effectively with colleagues of varying technical and non-technical abilities.

PREFERRED QUALIFICATIONS

  • GCP Machine Learning certification, Understanding of CPG industry
  • Exposure to Deep Learning/RL/LLMs
  • Publications or contributions to the data science and AI community.
  • Certifications in AI, machine learning, or related fields.




ELIGIBILITY

Applicants must meet minimum age qualifications in the country in which the job is located.

Facts

Source
Workday
External id
10123445-1
Location
Powai, Mumbai, MH, India
Remote
Hybrid, from the posting
Employment
Full time
Posted
3 Aug 2026
First seen
31 Oct 2026, 12:11
Last seen
1 Nov 2026, 09:00
Status
Not yet added
Posting id
f9d2ff9f-b925-5b64-9680-92c25837deb2