Staff Platform Engineer: AI-Scale Cloud Platform

Refinitiv; Frisco (TX); Hybrid; USD 102,000 - 190,000; Full time

Benefits offered by this job

Hybrid Work Model

Mental Health Days off

Tuition Reimbursement

Employee Stock Purchase Plan

Job summary

Thomson Reuters is seeking a Staff Platform Engineer for Materia AI to design and evolve cloud-native platforms powering AI agents and orchestration systems. You will lead architectural decisions, mentor colleagues, and champion best practices in a fast-paced enterprise environment.

You will build scalable, secure production infrastructure across multi-cloud environments, implement golden-path tooling, and establish robust monitoring and DORA metrics to optimize deployment velocity and quality.

Qualifications

• 8+ years of experience in backend development, platform engineering, production engineering, or SRE.
• Experience developing, deploying, and maintaining a backend service and client interfaces for product teams.
• Troubleshooting low-level DNS, TCP, IP, and TLS protocols.

Responsibilities

• Design and develop cloud-native platform components and CI/CD pipelines.
• Lead architectural decisions and mentor engineers.
• Build and maintain reference implementations and tooling to accelerate developer workflows.
• Ensure security, reliability, and observability of production systems.
• Collaborate with AI/ML engineers, developers, product managers, and SREs to deliver scaleable services.

Skills

Go

Python

Rust

Kubernetes

Terraform

CUE

GitHub Actions

Snyk

DataDog

AI/LLM

Education

Bachelor’s degree in computer science, computer engineering, or related field

Tools

Terraform

CUE

GitHub Actions

Playwright

Snyk

Job description

Thomson Reuters is seeking a Staff Platform Engineer for Materia AI to design and evolve cloud-native platforms powering AI agents and orchestration systems. You will lead architectural decisions, mentor colleagues, and champion best practices in a fast-paced enterprise environment.

You will build scalable, secure production infrastructure across multi-cloud environments, implement golden-path tooling, and establish robust monitoring and DORA metrics to optimize deployment velocity and quality.