Lead AI Engineer — Enterprise Systems & RAG Architect

Thomson Reuters; Eagan (MN); Hybrid; USD 127.400 - 236.600; Vollzeit

Benefits dieser Stelle

Hybrid Work Model

Competitive Benefits

Mental Health Days

Zusammenfassung

Thomson Reuters seeks a Lead Software Engineer, AI, to architect and deliver AI-driven software for investigators using CLEAR. Lead multi-component pipelines, RAG architectures, and autonomous AI agents while mentoring teams and shaping technical strategy.

You will work with AI researchers, engineers, and product partners to optimize latency, cost, and user experience, ensuring secure, scalable deployments across cloud-native platforms.

Qualifikationen

• Bachelor’s or higher in CS/Engineering or equivalent experience. Master’s preferred.
• Strong AI/ML foundation with experience in Generative AI, RAG, LLMs, deployment lifecycle and metrics.
• Proven backend services and API craftsmanship with distributed systems understanding.
• Expertise in AWS and orchestration tools; experience integrating AI models into production.

Aufgaben

• Build and architect AI-driven systems at enterprise scale including multi-component pipelines and RAG architectures.
• Drive innovation, evaluate cutting-edge AI techniques, and translate insights into technical roadmaps.
• Provide senior technical leadership, mentor engineers, and enforce governance for model lifecycle.

Kenntnisse

AI/ML leadership

Backend APIs

Leadership & mentoring

AWS cloud

Ausbildung

Bachelor’s degree in Computer Science/Engineering or equivalent

Master’s degree preferred

Tools

Docker

Kubernetes

Terraform

PyTorch

TensorFlow

JAX

Jobbeschreibung

Thomson Reuters seeks a Lead Software Engineer, AI, to architect and deliver AI-driven software for investigators using CLEAR. Lead multi-component pipelines, RAG architectures, and autonomous AI agents while mentoring teams and shaping technical strategy.

You will work with AI researchers, engineers, and product partners to optimize latency, cost, and user experience, ensuring secure, scalable deployments across cloud-native platforms.