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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Software Engineer III, .NET & Applied AI - **Company:** THE JUDGE GROUP, INC. - **Location:** Southlake, TX, United States - **Experience:** Experienced - **Salary:** $135,200.0 - $145,600.0 - **Contract:** Temporary to permanent - **Skills:** .NET Framework, Application Programming Interfaces (APIs), Agile Methodology, Artificial Intelligence, Amazon Web Services, Automation of Tests, Unit Testing, Behavior-Driven Development, Cloud Engineering, Software Quality, Continuous Integration, Data Stores, Software Debugging, Programming Tools, Distributed Systems, Fault Tolerance, IBM WebSphere MQ, PostgreSQL, Enterprise Messaging Systems, MongoDB, NoSQL, Systems Development Life Cycle, Reliability Engineering, E2e Testing, Software Engineering, Data Streaming, Systems Integration, Web Services, Transaction Processing (Computing), Aerospike, Google Cloud, Test-Driven Development (TDD), GitHub Copilot, Large Language Models, Event Driven Architecture, Kubernetes, Information Technology, Apache Kafka, Data Management, Splunk, Data Pipelines, Serverless Computing, Docker, Microservices - **Published:** September 29, 2026 - **Apply:** https://www.dice.com/job-detail/ed194494-3fea-4cb9-bb86-0de18ca62633 ## About the Role Bachelor's degree in Computer Science, Software Engineering, a related technical field, or equivalent practical experience. 5 years of software engineering experience developing distributed, high-volume applications or web services using .NET. 4 years of experience building and deploying cloud-native architectures utilizing Docker, Kubernetes, microservices, and CI/CD pipelines on platforms such as Google Cloud Platform, AWS, or PCF. 2 years of experience with relational or NoSQL data stores (e.g., PostgreSQL, MongoDB, Aerospike). Experience designing event-driven systems using distributed streaming and messaging platforms (e.g., Apache Kafka, IBM MQ). Hands-on experience integrating GenAI developer tools (e.g., GitHub Copilot, Claude Code, or LLM-driven IDE/CLI tooling) into day-to-day coding, unit testing, debugging, and documentation workflows. Preferred qualifications: Master's degree in Computer Science, Software Engineering, Artificial Intelligence, or a related technical discipline. Subject matter expertise in broker-dealer workflows, electronic trading systems, or enterprise capital markets domains. Demonstrated experience with Test-Driven Development (TDD), Behavior-Driven Development (BDD), and automated end-to-end integration testing. Deep expertise in reliability engineering, high-throughput caching, latency optimization, and distributed observability tools (e.g., Splunk, OpenTelemetry). Proven track record leading legacy-to-cloud modernization initiatives and establishing standard engineering practices for AI-augmented software development. Strong technical communication skills with experience mentoring engineering peers on distributed systems design and emerging AI productivity frameworks. ## Description As a Software Engineer specializing in .NET and Applied AI, you will help design and modernize mission-critical systems supporting high-volume, highly available enterprise financial services. You will build scalable distributed architectures, cloud-native services, and event-driven data pipelines while actively integrating generative AI tools and agentic workflows across the software delivery lifecycle. In this role, you will partner closely with product owners, architects, and engineering peers to convert complex domain requirements into resilient technical solutions, championing code quality, operational excellence, and responsible AI-assisted engineering practices., Architect, build, and modernize scalable, resilient, and event-driven microservices using .NET, messaging platforms (e.g., Kafka, IBM MQ), and cloud-native design patterns. Design, optimize, and maintain high-throughput APIs and distributed components supporting reliable transaction processing in a regulated financial domain. Accelerate development and testing velocity by embedding generative AI coding assistants, agentic workflows, prompt engineering, and spec-driven development across the SDLC. Drive engineering excellence through automated testing, peer reviews, CI/CD pipeline automation, observability, performance profiling, and production readiness. Modernize legacy monolithic systems into decoupled, cloud-based .NET services backed by modern relational and NoSQL data platforms. 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