Lead Machine Learning Engineer (MLops)

Overview

Lead ML Engineer role focused on migrating ML solutions from concept to production using MLOps practices. You will build scalable, resilient, automated ML operations on GCP and lead production support for ML systems.

What you'll do

• Design and build end-to-end MLOps pipelines on GCP using Vertex AI, Kubeflow, and Airflow.
• Automate model deployment, monitoring, retraining, logging, and ML pipeline orchestration.
• Set 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.
• Provide production support, troubleshoot issues, perform root cause analysis, and implement preventive actions.
• Partner with Data Science, Engineering, and Business teams to deliver production-ready ML solutions.
• Research and adopt emerging MLOps technologies to improve scalability and reduce cloud costs.

What you'll need

• 7+ years of relevant machine learning experience in MLOps.
• 12+ years of overall industry experience.
• Minimum bachelor’s degree and an advanced degree in a quantitative field (CS, engineering, statistics, math, or data science).
• Strong proficiency in Python, SQL/BigQuery, and Vertex AI on GCP.
• 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.

Nice to have

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