B2/C1 - AI Engineer

Capgemini; Charlotte (NC); On-site; USD 106,028,000 - 165,592,000; Full time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Retirement plan

Job summary

Tundra Technical Solutions in Charlotte, NC seeks a Senior AI Platform Engineer to design, build, and scale cloud infrastructure powering AI/ML workloads on GCP. You will own CI/CD pipelines and MLOps workflows to ensure reliable, secure production delivery.

Responsibilities include containerizing services with Docker and Kubernetes (GKE), applying Terraform IaC, establishing observability, and collaborating with data scientists to productionize models while enforcing security.

Qualifications

• 7+ years of experience in platform engineering, DevOps, or infrastructure engineering
• Hands-on experience with Google Cloud Platform (GCP) including Vertex AI and GKE
• Designing and managing CI/CD pipelines (Cloud Build, Jenkins, GitHub Actions, GitLab CI, ArgoCD)
• Proficiency with Infrastructure as Code (Terraform preferred)
• Strong scripting/programming skills (Python, Bash, or Go)
• Experience with containerization/orchestration (Docker, Kubernetes)
• Knowledge of MLOps concepts and ML lifecycle (training, versioning, deployment, monitoring)
• Familiarity with observability tooling (Cloud Monitoring, Prometheus, Grafana)
• Cloud security, IAM, and secrets management best practices

Responsibilities

• Design, build, and maintain scalable cloud infrastructure on GCP to support AI/ML workloads
• Architect and manage CI/CD pipelines for model training, deployment, and release workflows
• Build and maintain MLOps pipelines for model versioning, training, deployment, and monitoring
• Implement IaC (Terraform) for repeatable provisioning
• Containerize and orchestrate services with Docker and Kubernetes (GKE)
• Establish observability and alerting for AI/ML services
• Collaborate with AI/ML engineers to productionize models
• Ensure platform security and secrets management across environments

Skills

GCP & Vertex AI

CI/CD pipelines

Scripting (Python/Bash/Go)

Docker & Kubernetes

MLOps concepts

IAM & cloud security

Observability & monitoring

Tools

Terraform

GKE

Kubeflow

MLflow

Cloud Build / Jenkins / GitHub Actions / GitLab CI / ArgoCD

Prometheus & Grafana

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.

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.