Java Gen AI Developer

**Onsite schedule in Austin or Southlake. TX**

Job Description:

As a Senior Java AI Developer, this role will lead AI-focused workflows across the System Development Life Cycle. You will work at the intersection of Java engineering, event-driven architectures, cloud-native platforms, and Generative AI-assisted delivery to build intelligent, reliable systems that support Client clients and business capabilities.

Required Qualifications:

Core Technical Qualifications:

• Demonstrated experience leading the design and implementation of highly scalable, resilient, and event-driven applications using Java, Spring Boot, Kafka, and cloud-native architectures.
• Strong knowledge of distributed systems, stream processing, performance optimization, resiliency patterns, observability patterns, and high-availability application design.
• Experience designing and implementing RESTful services, microservices, asynchronous message flows, and integration patterns for high-volume enterprise platforms.
• Working experience with Kafka, IBM MQ or equivalent messaging platforms, Splunk or equivalent observability tools, JSON/XML, caching, performance analysis, profiling, and production troubleshooting.
• Experience being a technical lead for a team and/or project, including solution design, engineering standards, code review practices, and technical delivery ownership.

AI-Enabled Engineering Qualifications:

• Demonstrated hands-on experience using GenAI coding assistants across SDLC workflows, including implementation, refactoring, unit testing, regression support, code reviews, scripting, automation, troubleshooting, and documentation, while maintaining ownership for correctness, quality, and engineering validation.
• Practical familiarity with tools such as GitHub Copilot and/or Claude Code in IDE or CLI-based workflows, with the ability to apply AI assistance to reduce context switching and accelerate delivery beyond code generation.
• Working knowledge of agentic workflows, spec-driven development, custom instructions, and prompt engineering, with the ability to establish team-level practices for effective AI-assisted development.
• Ability to apply AI-assisted approaches responsibly for code generation, test generation, technical documentation, root cause analysis support, modernization analysis, and development workflow optimization.

Education and Experience:

• Bachelor s degree in Computer Science, Software Engineering, or related technical field, or equivalent practical experience.
• 7+ years of professional software engineering experience developing scalable, high-performance Java, cloud-based, and web services applications that support high-volume transactions in highly available environments.
• 6+ years of experience building and deploying cloud-native applications using Google Cloud Platform, Docker, Kubernetes, microservices, and CI/CD practices.
• Experience with cloud platforms and deployment environments such as PCF, AWS, or Google Cloud Platform.
• 2+ years of experience working with databases such as MongoDB, Aerospike, PostgreSQL, Oracle, or comparable relational and NoSQL platforms.
• 2+ years prior experience in IT transformation and system modernization initiatives from legacy platforms to distributed Java, cloud-native, and event-driven architectures.
• 2+ years of hands-on experience with AI/ML implementation, AI-assisted engineering practices, or production deployment of AI-enabled solutions.
• Hands-on experience or practical exposure to Large Language Models such as GPT, Claude, Gemini, or comparable enterprise AI platforms.
• Knowledge of Agile methodology and experience in an Agile working environment.
• Experience with Atlassian tool stack such as Jira and Confluence.
• Domain / Industry: Understands broker-dealer domain capabilities and operations, with preferred experience in the financial services industry.
• Communication: Communicates clearly and effectively, with proven ability to guide and collaborate with engineers in a team-oriented environment.
• Execution Focus: Brings a collaborative mindset and bias for action, effectively partnering with developers, architects, product owners, and adjacent teams to drive outcomes.

Preferred Qualifications:

• Master s degree in Information Technology, Computer Science, Software Engineering, Artificial Intelligence, or related degree, or equivalent practical experience.
• Experience in Test Driven Development, Behavior Driven Development, QA automation, and quality engineering mindset and behaviors.
• Experience with Spring Cloud, Spring Security, reactive programming, API gateways, service mesh, or comparable Java ecosystem capabilities.
• Experience with performance engineering, reliability testing, resiliency testing, and operational readiness practices for high-volume distributed platforms.
• Proficient in creating architecture diagrams, design documentation, functional and technical specifications, automated test process documentation, runbooks, and production/test environment documentation.
• Familiarity with Retrieval-Augmented Generation, vector databases, AI orchestration patterns, agent-based workflows, or enterprise AI platform integration is a plus.

Key Responsibilities:

• Lead design and development of Java-based, cloud-native, event-driven applications that are scalable, resilient, observable, and performant.
• Build and modernize services using Java, Spring Boot, Kafka, REST APIs, CI/CD pipelines, container platforms, and distributed system patterns.
• Partner with architects, developers, product owners, and cross-functional teams to translate business capabilities into high-quality technical solutions.
• Use GenAI-assisted development tools to accelerate implementation, refactoring, unit testing, documentation, troubleshooting, modernization analysis, and engineering workflow improvements.
• Establish responsible AI-assisted development practices, including reusable prompts, custom instructions, review expectations, validation standards, and team-level adoption patterns.
• Drive engineering quality through code reviews, automated testing, observability, resiliency validation, performance tuning, and production readiness practices.
• Mentor engineers on Java engineering standards, distributed architecture, cloud-native design, event streaming, and AI-augmented SDLC practices.
• Contribute to modernization initiatives by moving legacy capabilities toward distributed Java services, event-driven platforms, and cloud-native operating models.