AI Platform Engineer/DevOp

Capgemini; Charlotte (NC); On-site; USD 51,000 - 80,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Retirement plan

Job summary

Capgemini in Charlotte, NC seeks a senior AI Platform Engineer to build and operate the infrastructure powering AI/ML workloads. The role emphasizes cloud platform engineering, CI/CD automation, and MLOps to enable reliable, secure AI delivery.

You will design scalable GCP infrastructure, manage CI/CD pipelines, containerize services with Docker/Kubernetes, and ensure security, observability, and compliance across environments, collaborating with AI/ML teams.

Qualifications

• 7+ years in platform engineering, DevOps, or infra roles.
• Hands-on with Google Cloud Platform (GCP) incl Vertex AI and GKE.
• Design and manage CI/CD pipelines.
• IaC experience, Terraform preferred.
• Strong scripting in Python/Bash/Go.

Responsibilities

• Design, build, and maintain scalable cloud infrastructure on GCP.
• Architect and manage CI/CD pipelines for model training, deployment, and app releases.
• Build and maintain MLOps pipelines for versioning, training, deployment, and monitoring.
• Implement Infrastructure as Code for repeatable provisioning.
• Containerize and orchestrate services using Docker and Kubernetes (GKE).
• Establish observability, logging, and alerting for AI/ML services.
• Partner with AI/ML engineers to productionize models and streamline from experimentation to production.
• Ensure platform security, IAM policies, secrets management, and compliance across environments.
• Drive automation to reduce manual toil across build, test, deployment, and rollback processes.
• Troubleshoot platform/infrastructure issues and drive root-cause resolution for production incidents.

Skills

GCP expertise

CI/CD design

Python scripting

Kubernetes knowledge

Security and IAM

Observability & monitoring

MLOps lifecycle understanding

Automation mindset

Tools

Terraform

Docker

GKE

Cloud Run

Vertex AI

GitHub Actions

Jenkins

ArgoCD

Job description

We are seeking a senior AI Platform Engineer to build, operate, and scale the infrastructure and tooling that power AI/ML and generative AI workloads. This role focuses on cloud platform engineering, CI/CD automation, and MLOps practices that enable reliable, secure, and scalable delivery of AI systems.

Key Responsibilities:

• Design, build, and maintain scalable cloud infrastructure on GCP to support AI/ML and application workloads
• Architect and manage CI/CD pipelines for model training, deployment, and application release workflows (Cloud Build, GitHub Actions, Jenkins, GitLab CI, or similar)
• Build and maintain MLOps pipelines for model versioning, training, deployment, and monitoring (Vertex AI Pipelines, Kubeflow, MLflow)
• Implement Infrastructure as Code (Terraform, Deployment Manager) for repeatable, auditable environment provisioning
• Containerize and orchestrate services using Docker and Kubernetes (GKE)
• Establish observability, logging, and alerting for AI/ML services (Cloud Monitoring, Cloud Logging, Prometheus/Grafana)
• Partner with AI/ML engineers and data scientists to productionize models and streamline the path from experimentation to production
• Ensure platform security, IAM policies, secrets management, and compliance across environments
• Drive automation to reduce manual toil across build, test, deployment, and rollback processes
• Troubleshoot platform/infrastructure issues and drive root-cause resolution for production incidents

Required Qualifications:

• 7+ years of experience in platform engineering, DevOps, or infrastructure engineering roles
• Strong hands-on expertise with Google Cloud Platform (GCP) — Vertex AI, GKE, Cloud Run, IAM, VPC/networking, BigQuery
• Deep experience designing and managing CI/CD pipelines (Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD)
• Proficiency in Infrastructure as Code (Terraform preferred)
• Strong scripting/programming skills (Python, Bash, or Go)
• Experience with containerization and orchestration (Docker, Kubernetes)
• Solid understanding of MLOps concepts and the ML lifecycle (training, versioning, deployment, monitoring)
• Experience with observability tooling (Cloud Monitoring, Prometheus, Grafana, ELK/EFK)
• Strong understanding of cloud security, IAM, and secrets management best practices

Preferred Qualifications:

• GCP Professional certifications (Cloud DevOps Engineer, Cloud Architect, or Machine Learning Engineer)
• Experience with GitOps workflows (ArgoCD, Flux)
• Background working in regulated or enterprise-scale environments
• Familiarity with cost optimization and FinOps practices on GCP

The pay range that the employer in good faith reasonably expects to pay for this position is $36.98/hour - $57.79/hour. Our benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis.

Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.