Senior AI Platform Engineer: Cloud, MLOps & Reliability

Vizient, Inc.; Irving (TX); On-site; USD 102,000 - 179,000; Full time

Benefits offered by this job

Comprehensive benefits

Incentive pay

Job summary

Vizient, Inc. is seeking an experienced backend engineer to design and build scalable cloud-based systems powering AI/ML products. You will work with AI/ML engineers and data scientists to productionize LLM applications, RAG pipelines, and agentic AI workflows.

You will leverage modern cloud tech, IaC practices, and tooling to deliver secure, reliable platform capabilities, define reusable patterns, and establish operational best practices across the AI/ML team.

Qualifications

• Relevant degree preferred.
• 5+ years of relevant experience required.
• Strong Python development experience with production backend services and APIs.
• Hands-on experience with Pulumi, Terraform, and cloud platforms (AWS/Azure/GCP).
• Experience with Docker, CI/CD pipelines, Kubernetes, and cloud deployment practices.
• Familiarity with ML/AI systems, model serving, LLM apps, and data pipelines.

Responsibilities

• Design, develop, and maintain backend services, APIs, and platform components for AI/ML apps.
• Build scalable cloud infrastructure using Pulumi and IaC practices; develop CI/CD pipelines and containerized environments.
• Support production deployment of ML models, LLM apps, RAG pipelines, and agentic AI systems.
• Enhance observability through monitoring, logging, tracing, and incident management.

Skills

Python

Pulumi

Terraform

AWS/Azure/GCP

Docker

CI/CD

Kubernetes

Observability

ML/AI platforms

Education

Bachelor's degree preferred

Tools

Databricks

Azure AI Foundry

Job description

Vizient, Inc. is seeking an experienced backend engineer to design and build scalable cloud-based systems powering AI/ML products. You will work with AI/ML engineers and data scientists to productionize LLM applications, RAG pipelines, and agentic AI workflows.

You will leverage modern cloud tech, IaC practices, and tooling to deliver secure, reliable platform capabilities, define reusable patterns, and establish operational best practices across the AI/ML team.