Description

At this time, we are unable to offer visa sponsorship for this position. Candidates must be legally authorized to work in the United States (or applicable country) on a full-time basis without the need for current or future immigration sponsorship. Please note, we are not accepting candidates on H1B or OPT status

Overview

We are looking for an experienced DevOps Engineer to support our AI and ML initiatives, including GenAI platform development, deployment automation, and infrastructure optimization. You will play a critical role in building and maintaining scalable, secure, and observable systems that power scalable RAG solutions, model training platforms, and agentic AI workflows across the enterprise.

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:

6+ 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.

Position Type and Expected Hours of Work: Full Time

Disclaimer:

The above statement is intended to describe the general nature and level of work being performed. It is not intended to be an exhaustive list of responsibilities, duties and skills required.

Global Payments Inc.

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