Inference Engineer - AI

Job Description Thomson Reuters is seeking a Staff Inference Engineer, AI. This person will collaborate with platform teams to enhance capacity forecasting for AI workloads and work with Product, Data Science, Architecture, and Enterprise AI teams to onboard new research models into production.

Key Responsibilities Within Platform Engineering and Enterprise AI Services, an AI Inference Engineer is responsible for product ionizing, optimizing, and scaling AI and LLM workloads that power TR’s AI driven products.

This role ensures that our trained models—from classical ML to generative AI—run efficiently across TR’s multi cloud footprint (AWS, Azure, GCP, OCI), meet strict enterprise reliability requirements, and integrate seamlessly with our data backbone (Snowflake, Open Search vector search, API managed model routing).The successful candidate will help build the next generation of TR’s AI infrastructure, working alongside cloud engineering, data engineering, product teams, and AI Services.

Optimize LLMs and ML models for high-performance inference using techniques such as quantization, pruning, distillation, and hardware specific tuning

Deploy and scale inference workloads on GPUs across AWS, Azure, GCP and internal Kubernetes clusters, ensuring predictable performance during peak traffic hours, especially during business hours

Implement routing and failover strategies for OpenAI/Anthropic/Vertex AI traffic

Integrate models into production grade APIs supporting TR products and enterprise workflows.

Develop highly optimized environment and eliminate performance bottlenecks to reduce latency.

Collaborate with Platform Engineering teams (Landing Zones, Network, Storage, Compute, AI) to ensure inference workloads align with TR’s cloud native patterns (AWS, Azure, GCP, OCI)
Build and optimize containerized inference pipelines using Kubernetes for large-scale distributed workloads

Ensure compliance with TR’s AI standards for deployment, monitoring, governance, and drift detection

Profile inference performance, identify GPU/CPU bottlenecks, and optimize compute utilization across heterogeneous hardware

Implement observability and health monitoring for inference pipelines, ensuring reliability of enterprise AI services

Collaborates closely with AI engineers to invent new quantization techniques, improve numerical precision, and explore non‑standard architectures, and support the scale out of AI infrastructure during critical releases and global product rollouts

Partner with Cloud Engineers (Azure, AWS, GCP) to develop guardrails and automation that support inference workloads

Required Qualifications 7+ years of relevant experience

Strong understanding of ML/LLM fundamentals and inference optimization techniques.

Hands-on experience with GPU programming (CUDA preferred), inference runtimes (TensorRT, ONNXRuntime), and deep learning frameworks (PyTorch/Tensor Flow)
Proficiency in Python and at least one systems language (C++ strongly preferred for performance critical inference paths)
Experience deploying AI workloads to AWS/GCP/Azure and Kubernetes

Preferred Qualifications Familiarity with vector search systems (Open Search vectors) and retrieval augmented generation pipelines

Knowledge of distributed systems, microservices, CI/CD, and cloud native architecture#LI-MW1

New Position:
This position is open due to an existing vacancy to support our evolving business needs.

What’s in it For You?

Hybrid Work Model:
We’ve adopted a flexible hybrid working environment for our office-based roles while delivering a seamless experience that is digitally and physically connected.

Flexibility & Work-Life Balance:
Flex My Way is a set of supportive workplace policies designed to help manage personal and professional…