Description
Build and deploy production AI solutions that help tax and audit professionals improve accuracy, streamline complex work, and deliver better outcomes for their clients.
As a Senior Software Engineer, you will combine Python, backend engineering, large language models (LLMs), and client-facing delivery to turn business needs into secure, reliable applications.
Key Responsibilities
- Own client delivery on simpler builds and standard integrations, translating ambiguous needs into clear technical scope, working solutions, and effective handoffs for public accounting firms, corporate tax departments, and audit practices.
- Build production AI applications, including retrieval-augmented generation (RAG) pipelines, custom agents, and multi-step agentic workflows, for tax preparation and review, audit testing, workpaper automation, validation, and regulatory analysis.
- Develop scalable, secure, and maintainable backend services, applying architecture established with Staff and Principal engineers and partnering with Integration Engineers to connect solutions to Thomson Reuters products and client systems.
- Establish AI evaluation, observability, monitoring, tracing, benchmarking, and adversarial testing to improve solution accuracy, reliability, and production performance.
- Partner with Tax & Audit AI Engineers to translate tax rules and audit procedures into engineering plans, prototypes, and measurable evaluation criteria.
- Manage delivery across multiple engagements at agreed time allocations, contributing technical judgment during early discovery, rapid prototyping, and pre-sales discussions.
- Support deployments through post-launch stabilization, incident resolution, runbooks, and support handoffs, and contribute reusable components and engineering patterns that improve future delivery.
Required Qualifications
- 5+ years of software engineering experience, with a strong foundation in system design, distributed systems, and maintainable software architecture.
- At least 2 years of hands-on experience building and deploying AI/ML or LLM-powered applications in production.
- Strong proficiency in Python and backend engineering, including REST APIs, asynchronous services, and event-driven systems.
- Hands-on experience with RAG architectures, vector databases, and AI orchestration frameworks, such as LangChain, LangGraph, PydanticAI, or AutoGen.
- Experience deploying secure applications on AWS, Azure, or Google Cloud, using Infrastructure as Code and continuous integration and delivery (CI/CD), alongside observability and AI evaluation tooling.
- Ability to lead client discussions, explain technical tradeoffs clearly, solve problems in ambiguous environments, and own delivery across competing priorities, collaborating with both technical and non-technical stakeholders.
Benefits
- Hybrid Work Model: flexible hybrid working environment
- Flexibility & Work-Life Balance: supportive workplace policies
- Career Development and Growth: continuous learning and skill development
- Industry Competitive Benefits: comprehensive benefit plans
- Culture: award-winning reputation for inclusion and belonging, flexibility, work-life balance, and more
- Social Impact: opportunities to get involved with pro-bono consulting projects and Environmental, Social, and Governance (ESG) initiatives