Senior Lead Software Engineer - AI

Are you ready to shape the future of AI‑driven tax automation? We're looking for a Senior AI Engineer to build and scale intelligent workflows using modern LLM/ML tooling. Your work will directly impact how tax professionals automate complex, high‑stakes processes and access critical information. About the Role In this opportunity as a Senior Lead Software Engineer - AI , you will: Advance document intelligence:  Build generalizable models for tax documents (standard and non‑standard) using CV/NLP/LLMs and embeddings to move beyond fixed OCR templates with dynamic, context‑aware parsing. Boost auto‑verification & quality:  Improve native‑PDF/text‑layer matching, anomaly detection, and prior‑year‑aware checks to catch issues before human review; design human‑in‑the‑loop flows that preserve practitioner control. Scale the pipeline:  Productionize low‑latency training/inference pipelines over millions of documents with robust observability, evaluation, and drift monitoring. Integrate LLMs and traditional ML models into robust, auditable workflows that support tax determination, document processing, and rules‑based automation. Build and maintain data pipelines, feature extraction, and preprocessing for tax‑relevant data (e.g., invoices, filings, transactional data). Develop and integrate RESTful APIs / microservices to expose AI capabilities to internal and external systems. Ensure solutions meet compliance, security, and auditability requirements typical of tax and regulated domains. You’ll collaborate across SurePrep engineering and other Thomson Reuters product & engineering teams leveraging TR’s AI platforms including Materia and Additive to deliver reliable, auditable AI in production. About You You’re a fit for the role of Senior Lead Software Engineer - AI if you have the following required qualifications: 7 years in applied ML/AI  (or 5 with an advanced degree) delivering production systems in document AI/OCR/NLP or information extraction at scale. Strong Python and deep learning expertise  (PyTorch/TensorFlow), with experience in LLMs, embeddings/vector search, prompt design/evaluation, and statistical methods for quality and uncertainty. Cloud‑native ML ops experience:  training/inference pipelines, feature/data stores, observability, cost/perf optimization. Own end‑to‑end lifecycle for AI features: design → implementation → testing → deployment → monitoring → iteration. Optimize model/workflow inference for latency, throughput, and cost, including caching, batching, and model selection strategies. Implement monitoring, logging, tracing, and alerting for workflows and models in production (data drift, model performance, error rates). Debug and troubleshoot complex AI systems in production, including prompt failures, integration bugs, and data quality issues. Partner with Product, Tax SMEs, and other engineering teams to translate business/tax requirements into AI workflows. Collaborate with data scientists and ML engineers on model selection, evaluation, and experimentation within Reducto pipelines. Lead design reviews, perform code reviews, and mentor mid‑level/junior engineers on AI best practices and production‑grade coding. Contribute to internal standards, best practices, and templates for building AI workflows with Reducto. Document AI background:  layout parsing, table extraction, doc‑type classification, native‑PDF matching, and verification automation. Agentic/LLM orchestration experience:  familiarity with TR AI platforms such as Materia and Additive; retrieval‑augmented and tool‑use patterns; evaluation and guardrails. Modern Development Practices: Hands-on with modern AI coding assistants including Claude Code, Cline, and GitHub Copilot for rapid prototyping, code generation, and workflow automation. Version control: Experience with version control (Git), testing frameworks (pytest or similar), and modern DevOps practices. Software Engineering : Familiarity with C#, Java or any oth ... (truncated, view full listing at source)