Senior Inference Engineer - AI

Thomson Reuters
RemotePosted 25 April 2026

Job Description

New Position: This position is open due to an existing vacancy to support our evolving business needs. Thomson Reuters is seeking a Senior Inference Engineer, AI. This person will c ollaborate 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. About the Role As a Senior Inference Engineer, AI , responsibilities include/you will: Within Platform Engineering and Enterprise AI Services , an AI Inference Engineer is responsible for productionizing, 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, OpenSearch 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 About You You are a potential fit for the role, Senior Inference Engineer, AI , if your background includes: 5 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, ONNX Runtime), and deep learning frameworks (PyTorch/TensorFlow) 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 Familiarity with vector search systems (OpenSearch vectors) and retrieval augmented generation pipelines Knowledge of distributed systems, microservices, CI/CD, and cloud native architecture #LI-MW1 What’s in it For You? Hybrid Work Model: We’ve adopted a flexible hybrid working environment (2-3 days a week in the office depending on the role) 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 responsibilities, whether caring for fam ... (truncated, view full listing at source)
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