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Tier-1 brand and metro location, but specialized MLOps/GCP skills reduce candidate pool.
MLOps/GCP skills generally transferable, but Vertex AI and CPG preference increase domain specificity.
Explicit 6+ years, 3+ years AI, and mandatory GCP/Vertex, Airflow, Python, and container skills.
Own and implement end-to-end MLOps pipelines on GCP using Vertex AI, Airflow/Kubeflow for production ML solutions.
Manage ML model lifecycle including deployment, monitoring, performance optimization, and retraining.
Collaborate with cross-functional teams to ensure scalable, sustainable data and model pipelines and recommend automation improvements.
Bachelor’s degree (full time).
6+ years analytical experience with at least 3+ years in AI and Machine Learning.
Proficiency in BigQuery/SQL, Python, Vertex AI, GCP Services, Airflow/Cloud Composer/Kubeflow, and building custom containers.
Strong communication skills; passion for agile processes and data-driven development.
Experience operating in cloud-based ML engineering environments specifically on GCP with Vertex AI and pipeline orchestration tools.
Ability to work on scalable, enterprise-grade ML solutions involving multiple teams and technologies.
Demonstrated expertise in ML model lifecycle management and automation of MLOps best practices and standards.