Sr. Manager, AI/ML Ops Support

Job Title: MLOps & AI Lead GCP & Vertex AI

Experience Level : Senior / Lead (11-15 years)

About the Role:
We are seeking an experienced and innovative MLOps & AI Lead to drive our AI/ML initiatives on Google Cloud Platform (GCP), leveraging Vertex AI and associated GCP services. This role combines technical expertise, strategic vision, and leadership to design, deploy, and operationalize machine learning models at scale while ensuring best practices in governance, monitoring, and automation.

Key Responsibilities:

• Lead the design, deployment, and scaling of machine learning models using Vertex AI and other GCP AI/ML services.
• Define and implement MLOps pipelines for continuous integration, continuous delivery, and model monitoring.
• Collaborate with data engineers, data scientists, and business stakeholders to translate business problems into ML solutions.
• Optimize ML workflows for performance, scalability, and cost-efficiency in the cloud environment.
• Establish best practices for model versioning, reproducibility, testing, and CI/CD automation.
• Drive AI governance, monitoring, and explainability initiatives for responsible AI practices.
• Mentor and guide team members on MLOps, Vertex AI, and advanced AI technologies.
• Evaluate and integrate emerging AI/ML tools and frameworks to improve productivity and model performance.

Required Qualifications:

• 11-15 years of overall experience. And 5+years in AI/ML, MLOps, or ML engineering on cloud platforms, preferably GCP.
• Strong expertise in Google Cloud Platform, including Vertex AI , BigQuery, Dataflow, Cloud Storage, Cloud Functions, Cloud Run, AI Platform Pipelines, and related services.
• 5+ years of hands-on experience with MLOps practices: CI/CD for ML , model deployment, monitoring, and lifecycle management (MLflow).
• 5+ years of experience of code management and reliability using GitHub, pytest, and sonarqube.
• 3+ years of experience with Tableau.
• Proficiency in Python and common ML libraries (TensorFlow, PyTorch, etc.).
• Experience with workflow orchestration tools (Astronomer, Airflow, or similar).
• Knowledge of containerization (Docker, Kubernetes) and serverless architecture.
• Strong understanding of model evaluation, hyperparameter tuning, and performance optimization.
• Excellent communication skills and ability to translate complex ML concepts for technical and non-technical stakeholders.

Preferred Qualifications:

• GCP certifications (Professional Machine Learning Engineer, Professional Data Engineer).
• Experience with feature stores, MLOps monitoring tools, and AI explainability frameworks.
• Familiarity with DevOps practices, infrastructure as code (Terraform, Deployment Manager), and cloud security.