Staff AI Engineer — Enterprise-Scale RAG & Agents (Hybrid)

USA018 Thomson Reuters Holdings Inc.; Frisco (TX); Híbrido; USD 127.000 - 237.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Two mental health days off

Headspace app access

Retirement savings

Tuition reimbursement

Employee incentive programs

Wellbeing resources

Descripción de la vacante

Thomson Reuters is seeking a Staff Software Engineer, AI to lead the development of intelligent systems powering expert solutions in legal, tax, risk and compliance. You will drive AI strategy, collaborate with senior stakeholders and cross-functional teams to design and scale enterprise-grade AI platforms.

The role emphasizes leadership in AI/ML, architecture, and deployment of production-grade AI systems with cloud-native architectures and strong technical governance.

Formación

• Bachelor's degree in Computer Science, Engineering or equivalent experience.
• 8+ years of software engineering experience, including 4+ years in senior/lead AI/ML roles.
• Experience with Generative AI, RAG and LLMs.

Responsabilidades

• Design and lead the build-out of enterprise-scale AI systems with multi-component pipelines and autonomous agents.
• Advance AI model adaptation and customization for specialized domains (legal, tax, risk, compliance).
• Define technical standards and best practices for AI system architecture.

Conocimientos

AI/ML leadership

Generative AI

RAG architectures

LLMs

Python

PyTorch

TensorFlow

Cloud platforms

Distributed systems

Stakeholder communication

Educación

Bachelor's degree in CS/Engineering

Master's degree

Herramientas

Docker

Kubernetes

Terraform

APIs

Descripción del empleo

Thomson Reuters is seeking a Staff Software Engineer, AI to lead the development of intelligent systems powering expert solutions in legal, tax, risk and compliance. You will drive AI strategy, collaborate with senior stakeholders and cross-functional teams to design and scale enterprise-grade AI platforms.

The role emphasizes leadership in AI/ML, architecture, and deployment of production-grade AI systems with cloud-native architectures and strong technical governance.