AI Ops Engineer at Global Payments

Alpharetta, Georgia, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

FinTech, PaymentsIndustries

Requirements

4+ years of DevOps, AI Ops, 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
Ability to work with a high level of initiative, accuracy, and attention to detail
Ability to prioritize multiple assignments effectively and meet established deadlines
Ability to successfully, efficiently, and professionally interact with staff and customers
Excellent organization skills and 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

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.)

Skills

Key technologies and capabilities for this role

TerraformDockerKubernetesAWSGCPPrometheusGrafanaDatadogPineconepgVectorRedisMongoDB AtlasFeatureformCI/CDRAG

Questions & Answers

Common questions about this position

What salary is offered for the AI Ops Engineer position?

This information is not specified in the job description.

Is this AI Ops Engineer role remote or does it require office work?

This information is not specified in the job description.

What are the must-have skills for the AI Ops Engineer role?

Must-have skills include 4+ years of DevOps, AI Ops, or infrastructure engineering experience (preferably 2+ years in AI/ML), hands-on experience with cloud-native services like AWS Bedrock/SageMaker or GCP Vertex AI and GPU infrastructure, strong skills in CI/CD tools like GitHub Actions or ArgoCD, proficiency in scripting like Python or Bash, experience with monitoring systems, and deep understanding of Kubernetes.

What is the company culture like at Global Payments for this role?

The company features a dynamic team driven by passion for success, focused on delivering best-in-class payment technology and software solutions, with collaboration among data scientists, AI/ML engineers, and platform team members.

What makes a strong candidate for the AI Ops Engineer position?

A strong candidate has 4+ years of DevOps or AI Ops experience, preferably with AI/ML exposure, hands-on cloud and GPU skills, CI/CD proficiency, Kubernetes expertise, and bonus points for AI Ops tooling like MLflow or Kubeflow.