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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Staff Software Engineer - AI - **Company:** Reuters America LLC - **Location:** Frisco, TX, United States - **Experience:** Expert - **Salary:** $146,800.0 - $272,600.0 - **Contract:** Permanent contract - **Skills:** AI Evaluation, ASP.NET, .NET Framework, Application Programming Interfaces (APIs), Artificial Intelligence, Akka (Toolkit), Amazon Web Services, Systems Engineering, Automated Storage and Retrieval Systems, Audit Trail, Automation of Tests, C Sharp (Programming Language), Computer Engineering, Relational Databases, Distributed Systems, Github, High-Level Architecture, Information Extraction, Python (Programming Language), PostgreSQL, Machine Learning, Performance Tuning, Systems Development Life Cycle, OpenAI, Software Safety, Search Technologies, Software Engineering, Systems Integration, TypeScript, Web Application Frameworks, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Database Optimization, Backend, Agentic-AI, Rate Limiting, Build Management, AngularJS, Information Technology, Graphql, Data Management, Restful APIs, Model Context Protocol, Data Pipelines, Automation Anywhere, Docker, Microservices - **Published:** October 2, 2026 - **Apply:** https://diversityjobs.com/main/sendform/8/8/28176/1/18512902?backUrl=%2Fcareer%2F18512902%2FStaff-Software-Engineer-Ai-Texas-Frisco ## About the Role * Bachelor's degree in Computer Science, Computer Engineering, a related field, or equivalent professional experience. * 7+ years of progressive software engineering experience, including hands-on development, architecture, and delivery of large-scale production systems. * Strong, recent, hands-on C#/.NET production development experience, including building backend services and APIs using C#/.NET and ASP.NET Core or similar technologies. * Hands-on Python experience for AI/ML system development, including model integration, AI orchestration, APIs, data pipelines, or comparable production use cases. * Demonstrated experience personally building and delivering AI/ML features into production, including LLM-powered applications, AI agents, retrieval/RAG, orchestration, or comparable AI-native capabilities. * Strong hands-on platform and distributed-systems engineering experience, including microservices, system integration, data modeling, REST or GraphQL APIs, relational databases, observability, resilience, scalability, and performance optimization. * Experience implementing appropriate AI safety controls, guardrails, evaluation, reliability, and production operational practices. * Proven experience leading complex engineering initiatives from architecture and hands-on implementation through deployment, rollout, and long-term production operation. * Proven ability to operate as a hands-on Staff-level technical leader, providing technical direction and mentoring engineers while continuing to actively design, code, build, and deliver production software. * Strong communication and cross-functional collaboration skills with the ability to partner effectively with engineering, product, design, user experience, research, and machine learning teams. Candidates must have hands-on production AI delivery experience. AI experience limited to personal projects, side projects, coursework, experimentation, or proof-of-concept work alone does not meet this requirement., * Hands-on experience integrating LLM APIs such as OpenAI or Anthropic into production applications, including prompt and response management, cost controls, reliability, and safety considerations. * Experience with vector databases, embeddings, semantic search, and custom retrieval pipelines. * Experience designing automated testing, AI evaluation, reliability, and release practices for systems with non-deterministic model behavior. * Familiarity with actor-model architectures and frameworks, such as Microsoft Orleans or Akka, for resilient, message-driven distributed systems. * Experience with AWS, containerized microservices using Docker and Kubernetes, and CI/CD pipelines such as GitHub Actions or similar technologies. * Experience with modern JavaScript frameworks, particularly Angular and TypeScript. * Experience working in industries where correctness, auditability, security, and regulatory compliance are important, such as financial technology, audit, tax, or legal technology. * Experience interacting directly with customers and translating customer or business needs into scalable technical solutions. ## Description You will design and build C#/.NET backend and distributed systems and use Python to develop and integrate AI/ML capabilities, including large language model (LLM)-powered applications, AI agents, orchestration, retrieval, and document understanding. You will partner with Principal Engineers and cross-functional teams to establish AI engineering patterns, develop Model Context Protocol (MCP) servers and agent-based capabilities, and build reliable and scalable AI infrastructure., * Architect, develop, and deliver production backend and platform services using C#/.NET, PostgreSQL, AWS, microservices, and distributed-system patterns. * Build production AI/ML capabilities using Python, including LLM integration, AI agents, orchestration, retrieval, and multi-step AI workflows. * Lead hands-on AI engineering initiatives across product and infrastructure, including agent-based workflows, retrieval systems, and AI-assisted document processing. * Build and evolve AI orchestration capabilities, including routing, tool calling, MCP servers, multi-step workflows, safety controls, guardrails, evaluation, and resilient error handling around third-party LLMs. * Design reliable, scalable, high-throughput distributed systems and AI workloads, incorporating caching, queuing, rate limiting, model failover, observability, resilience, and cost/performance optimization. * Develop retrieval and data capabilities using document search, vector stores, embeddings, semantic search, and indexing strategies for large-scale audit and confirmation use cases. * Provide hands-on Staff-level technical leadership by setting technical direction, influencing engineering standards, mentoring engineers, and continuing to personally design, code, build, and deliver production systems.