Thomson Reuters

SeniorSoftwareEngineer,AI(C#,Cloud)

$140–140k Toronto, Ontario, Canada FULL TIME Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Senior candidates.

The Brief

“Senior Software Engineer, AI (C#, Cloud) at Thomson Reuters. Skills: Machine learning, Deep learning, Inference optimization, Cloud native. Optimize LLMs and ML models. Deploy inference workloads on GPUs”

Industry & Context.

Problems you'll solve

Performance optimizations; Eliminate performance bottlenecks; Identify GPU/CPU bottlenecks; Optimize compute utilization

What They're Looking For.

Must Have

ML/LLM fundamentals, AI workloads to AWS/GCP/Azure and Kubernetes, C#, GPU programming, Inference runtimes, Deep learning frameworks, Python, Systems language, Vector search systems, Retrieval augmented generation pipelines, Distributed systems, Microservices, CI/CD, Cloud native architecture, AI networks, GPU, Multithreading, Accelerators with vectorized instructions, Model compression, Hardware aware model optimizations, Hardware accelerators architecture, GPU/ASIC architecture, Machine learning compilers, High performance computing, Performance optimizations, Numerics, SW/HW co-design

Nice to Have

3+ years production experience deploying ML/LLM models at scale, Managing GPU fleets or inference clusters, Supporting enterprise grade AI workloads

What You'll Do.

Optimize LLMs and ML models

Deploy inference workloads on GPUs

Scale inference workloads on GPUs

Implement routing strategies

Implement failover strategies

Integrate models into production grade APIs

Develop highly optimized environments

Eliminate performance bottlenecks

Collaborate with Platform Engineering teams

Ensure inference workloads align with TR’s cloud native

Build containerized inference pipelines

Optimize containerized inference pipelines

Ensure compliance with TR’s AI standards

Profile inference performance

Identify GPU/CPU bottlenecks

Optimize compute utilization

Implement observability for inference pipelines

Implement health monitoring for inference pipelines

Enhance capacity forecasting for AI workloads

Onboard new research models into production

Invent new quantization techniques

Improve numerical precision

Explore non-standard architectures

Develop guardrails for inference workload

Develop automation for inference workload

Support scale out of AI infrastructure

Support global product rollouts

How You'll Work.

Team & Collaboration

Platform Engineering teams; Product teams; Data Science teams; Architecture teams; Enterprise AI teams; AI engineers; Cloud Engineers

Full Job Description

Thomson Reuters is seeking a Senior Software Engineer, AI (C#, Cloud). This role is for someone who has specialized experience in machine learning/deep learning domains such as model compression, hardware aware model optimizations, hardware accelerators architecture, GPU/ASIC architecture, machine learning compilers, high performance computing, performance optimizations, numerics or SW/HW co-design. **About the Role** As a **Senior Software Engineer, AI (C#, Cloud)** , you will: * 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. * Collaborate with platform teams to enhance capacity forecasting for AI workloads * Work with Product, Data Science, Architecture, and Enterprise AI teams to onboard new research models into production * Collaborates closely with AI engineers to inv

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