Senior Inference Engineer - AI

Thomson Reuters•Toronto, ON

1d•$110,000 - $204,200•Hybrid

About The Position

Thomson Reuters is seeking a Senior 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. 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

Requirements

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

Responsibilities

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

Benefits

Hybrid Work Model
Flexibility & Work-Life Balance
Career Development and Growth
Industry Competitive Benefits
Culture
Social Impact
Making a Real-World Impact
flexible vacation
two company-wide Mental Health Days off
access to the Headspace app
retirement savings
tuition reimbursement
employee incentive programs
resources for mental, physical, and financial wellbeing
market competitive health, dental, vision, disability, and life insurance programs
competitive 401k plan with company match
competitive vacation, sick and safe paid time off
paid holidays (including two company mental health days off)
parental leave
sabbatical leave
optional hospital, accident and sickness insurance paid 100% by the employee
optional life and AD&D insurance paid 100% by the employee
Flexible Spending and Health Savings Accounts
fitness reimbursement
access to Employee Assistance Program
Group Legal Identity Theft Protection benefit paid 100% by employee
access to 529 Plan
commuter benefits
Adoption & Surrogacy Assistance
Tuition Reimbursement
access to Employee Stock Purchase Plan