DevOps Engineer (AI/ML)

Every day Global Payments makes it possible for millions of people to move money between buyers and sellers using our payments solutions for credit debit prepaid and merchant services. Our worldwide team helps over 3 million companies more than 1300 financial institutions and over 600 million cardholders grow with confidence and achieve amazing results. We are driven by our passion for success and we are proud to deliver best-in-class payment technology and software solutions. Join our dynamic team and make your mark on the payments technology landscape of tomorrow.

RESPONSIBILITIES

• Design and implement CI/CD pipelines for AI and ML model training evaluation and RAG system deployment (including LLMs vectorDB embedding and reranking models governance and observability systems and guardrails).

• Provision and manage AI infrastructure across cloud hyperscalers (AWS/GCP) using infrastructure-as-code tools -strong preference for Terraform-.

• Maintain containerized environments (Docker Kubernetes) optimized for GPU workloads and distributed compute.

• Support vector database feature store and embedding store deployments (e.g. pgVector Pinecone Redis Featureform. MongoDB Atlas etc).

• Monitor and optimize performance availability and cost of AI workloads using observability tools (e.g. Prometheus Grafana Datadog or managed cloud offerings).

• Collaborate with data scientists AI/ML engineers and other members of the platform team to ensure smooth transitions from experimentation to production.

• Implement security best practices including secrets management model access control data encryption and audit logging for AI pipelines.

• Help support the deployment and orchestration of agentic AI systems (LangChain LangGraph CrewAI Copilot Studio AgentSpace etc.).

Must Haves:

• 4 years of DevOps or infrastructure engineering experience. Preferably with 2 years in AI/ML environments.

• Hands-on experience with cloud-native services (AWS Bedrock/SageMaker GCP Vertex AI or Azure ML) and GPU infrastructure management.

• Strong skills in CI/CD tools (GitHub Actions ArgoCD Jenkins) and configuration management (Ansible Helm etc.).

• Proficient in scripting languages like Python Bash -Go or similar is a nice plus-.

• Experience with monitoring logging and alerting systems for AI/ML workloads.

• Deep understanding of Kubernetes and container lifecycle management.

Bonus Attributes:

• Exposure to MLOps tooling such as MLflow Kubeflow SageMaker Pipelines or Vertex Pipelines.

• Familiarity with prompt engineering model fine-tuning and inference serving.

• Experience with secure AI deployment and compliance frameworks

• Knowledge of model versioning drift detection and scalable rollback strategies.

Abilities:

• Ability to work with a high level of initiative accuracy and attention to detail.

• Ability to prioritize multiple assignments effectively. Ability to meet established deadlines.

• Ability to successfully efficiently and professionally interact with staff and customers.

• Excellent organization skills.

• Critical thinking ability ranging from moderately to highly complex.

• Flexibility in meeting the business needs of the customer and the company.

• Ability to work creatively and independently with latitude and minimal supervision.

• Ability to utilize experience and judgment in accomplishing assigned goals.

• Experience in navigating organizational structure.

Global Payments Inc. is an equal opportunity employer. Global Payments provides equal employment opportunities to all employees and applicants for employment without regard to race color religion sex (including pregnancy) national origin ancestry age marital status sexual orientation gender identity or expression disability veteran status genetic information or any other basis protected by law. If you wish to request reasonable accommodations related to applying for employment or provide feedback about the accessibility of this website please contact .