Principal Software Engineer – AI Platform & Architecture

Thomson Reuters•Frisco, TX

1d•$158,900 - $340,000•Hybrid

About The Position

Thomson Reuters products power the professionals who are shaping the world. Our platforms drive legal, tax, accounting and audit products, including Cloud Audit Suite—a connected set of products spanning Engagement Manager, Guided Assurance, and Confirmation. As a Principal Software Engineer for Cloud Audit Suite, you will be the technical architect and hands-on design leader for our product platforms, integrating multiple products and technology stacks. You will also help drive our shift to an engineering-led, AI-native organization, one where AI writes much of the code and engineers stay in the loop to provide judgment, architecture, and direction where it matters most. Own the architecture, system design and AI strategy: You will be the principal engineer responsible for the architecture and system design of Cloud Audit Suite and new integrations to Confirmation and CoCounsel Audit via agents, APIs, MCP connections and shared components and repositories. Drive AI strategy for Cloud Audit Suite and help deliver the company's AI strategy. Set the technical direction for how we build MCP servers, design agents, find novel uses of LLMs, run experimentation, and stand up scalable solutions. Your decisions will influence multiple product lines, not just a single feature. Work at real production scale: Build and evolve systems that operate over millions of documents, structured and unstructured data, audit compliance rules, and thousands of concurrent AI interactions from accountants doing time‑sensitive work. Small teams, big surface area: Interact and lead across several teams of engineers, product, UX design and AI Labs partners to research, design and ship products quickly, with direct access to product leadership and customers.

Requirements

Bachelors Degree in Computer Science, Computer Engineering, Related Field, or Equivalent Experience
8-10+ years of experience in full-stack application development, including hands-on architecture and design leadership for large-scale systems, building scalable web services and APIs.
C#, .NET expertise and experience with production systems using frameworks like ASP.NET Core (or similar), relational databases (SQLServer, PostgreSQL or equivalent), and a major cloud provider (AWS preferred, Azure).
Experience with Python in AI/ML system development, including model integration, data pipelines, or API-based orchestration
Familiarity with actor model patterns and frameworks (Orleans, Akka, etc.) for resilient, message-driven distributed architectures
Experience architecting and implementing accessible front-end solutions, including: JavaScript, Typescript, HTML, CSS; experience with modern JavaScript frameworks, e.g., Angular, React, etc.
Demonstrated experience and passion for AI systems and new engineering paradigms: LLMs, agents, retrieval, or similar – you care about what “AI‑native” software should look like and know what guardrails to put in place.
Strong background in distributed systems and cross-system integration: data modeling, API contracts, integrating across heterogeneous services and technology stacks, observability, resilience patterns, and performance tuning under load.
Proven track record owning large, complex projects end‑to‑end: architecture, execution, rollout, and long‑term support and maintenance.
Excellent communication skills and the ability to partner with product, design, UX design and ML teams in a fast‑moving environment.

Nice To Haves

Experience designing and implementing CI/CD pipelines (i.e., GitHub Actions)
Hands‑on experience integrating LLM APIs (e.g., OpenAI, Anthropic) into production applications, including prompt/response management, cost controls, and safety considerations.
Experience with AI‑adjacent infrastructure: vector databases, embeddings, semantic search, or custom retrieval pipelines.
Experience having direct interaction and communications with customers.
Opinions and experience around automated testing, reliability, and release practices for systems with nondeterministic model behavior.
Prior experience in domains where correctness, auditability, and compliance matter (fintech, tax, audit, legal, or similar), or strong interest in applying AI in those contexts.

Responsibilities

Technical leadership and cross‑functional influence
Partner with the CoCounsel Audit Principal Engineer to determine the best ways to integrate Cloud Audit Suite (CAS) and CoCounsel Audi via MCP servers and tools, shared APIs, and agent-to-agent (A2A) integrations.
Lead multi‑quarter initiatives that cut across product engineering, product, labs, UX design and infrastructure/operations teams.
Mentor staff and senior engineers, raising the bar on system design, code quality, and AI-native engineering practices, including AI-assisted and AI-generated development with humans in the loop, across the org.
Be the expert in new model capabilities and scalable AI integration strategies, collaborating closely with AI/ML engineers, researchers, designers, and PMs to translate industry improvements into reliable, user‑facing workflows that accountants trust.
Help shape the team’s roadmap, technical strategy, and engineering culture – from experimentation practices to design, coding, testing, rollout, support and postmortems.
Design and own AI‑first backend systems
Own the end-to-end architecture and design of backend services (C#/.NET, Python, FastAPI, PostgreSQL, AWS, Vercel) that integrate and orchestrate across heterogeneous systems and technology stacks, powering generative AI agents, complex workflows and document‑centric experiences.
Build and evolve AI orchestration: routing, tool calling, MCP servers, multi‑step workflows, safety and guardrails, and robust error handling around third‑party LLMs (OpenAI, Anthropic, and others).
Scale, reliability, compliance and performance
Design for high‑throughput, low‑latency AI workloads: caching, queuing, rate‑limiting, model failover, and cost/performance tradeoffs.
Work with large‑scale data: millions of documents, retrieval and search, vector stores, and indexing strategies tailored to tax, account and audit use cases.
Help establish and refine SLOs, observability, and incident response for AI systems that must be correct, auditable, and trustworthy in professional workflows.