Senior AI Engineer

Capgemini • Atlanta, GA

18h • Hybrid

About The Position

Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world.LocationThis is a hybrid role based across multiple locations - Atlanta, Nashville, Chicago, Dallas, New jerseyAbout the job you're considering The Capgemini Google Cloud team offers extensive career opportunities and provides mentoring and coaching for teammates. The Senior AI Engineer role is for an experinrced professional with expertise in Vertex AI MLOps, Traditional ML and supervised learning models. Your role
The core of the project relies on Google Cloud Platform (GCP) and specifically Vertex AI
Self-directed, disciplined, and self-aware to balance project development and support activities with a commitment to excellence in quality and communication
Builds effective relationships and communicates with business partners and vendors to collect and clarify business requirements

Requirements

• Senior candidate with 8 plus years relevant experience in Cloud Platform & AI Services (Google Cloud/Vertex AI).
• Vertex AI Capabilities: Using built-in features for predictive and generative AI, as well as the Model Garden to discover and customize models.
• Vertex AI Pipelines: Building and executing steps in Vertex Pipelines, including understanding how to use Kubeflow to build pipeline templates for interoperability,.
• Core GCP Services: Configuring and enabling APIs for BigQuery, Google Cloud Storage, and managed Vertex AI notebooks.
• Good experience in MLOps & Automation - A major focus of the engagement is establishing Machine Learning Operations (MLOps) maturity.
• CI/CD Integration: Implementing Continuous Integration/Continuous Delivery workflows using Cloud Build or GitHub to deploy pipeline components,.
• Model Lifecycle Management: Managing models through the Vertex AI Model Registry, using Artifact Registry for Docker images, and setting up Vertex AI Experiments for tracking pipeline runs.
• Infrastructure as Code (IaC): Developing automation scripts (likely Terraform, though "IaC" is the term used) to manage the lifecycle of AI/ML sandbox projects and environments.

Benefits

• Paid time off based on employee grade (A-F), defined by policy: Vacation: 12-25 days, depending on grade, Company paid holidays, Personal Days, Sick Leave
• Medical, dental, and vision coverage (or provincial healthcare coordination in Canada)
• Retirement savings plans (e.g., 401(k) in the U.S., RRSP in Canada)
• Life and disability insurance
• Employee assistance programs
• Other benefits as provided by local policy and eligibility

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed