Java AI Developer

Saxon Global•Denver, CO

1d•Hybrid

About The Position

We are seeking two AI Enablement Engineers with a Java focus to join our team. This long-term, hybrid role requires 3 days per week onsite in either Denver/Englewood, CO or Minneapolis, MN. The primary goal is to enable effective use of approved AI-assisted development tools, such as enterprise copilots, internal GenAI tooling, and approved LLM platforms. You will develop and maintain reusable prompt patterns, agent workflows, and repository-level guidance to standardize AI-assisted development across teams. A key aspect of this role involves partnering with internal AI governance and architecture groups to pre-align use cases, reduce approval friction, and avoid rework. Additionally, you will apply AI-assisted techniques to analyze legacy codebases, extract business rules, and document undocumented behavior. You will support the decomposition of monolithic applications into domain-aligned, API-first architectures using AI-driven dependency analysis and assist teams modernizing legacy stacks including mainframe, batch, and large Java-based systems. You will also enable AI-assisted generation of various types of tests (unit, integration, contract, regression) and help integrate these AI-generated tests into existing CI/CD pipelines without compromising quality, compliance, or auditability. This role aims to improve test coverage and reduce manual testing toil across modernization programs. Finally, you will act as a hands-on coach for engineers and tech leads on compliant, effective AI usage, creating lightweight training materials, examples, and playbooks tailored to real delivery scenarios, and embedding with teams temporarily to drive adoption.

Requirements

Practical experience using AI tools for code analysis, generation, documentation, or testing.
Experience in Java and Spring Boot development.
Strong knowledge of microservices architecture and API design principles.
Experience with RESTful services, JSON, and YAML.
Familiarity with CI/CD pipelines (Jenkins, Maven, Git).
Understanding of security best practices and performance tuning.
Proven ability to operate effectively in regulated, risk-driven enterprise environments.
Strong communication skills and the ability to teach and influence without formal authority.

Nice To Haves

Strong background in software engineering with hands-on experience modernizing large, legacy systems.
Experience modernizing Java-based monoliths into Spring Boot or similar API architectures.
Experience with COBOL, mainframe environments, or large-scale batch processing systems.
Familiarity with containerized or cloud-based deployment models.
Knowledge of PCI, model risk governance (MRM), and banking compliance controls.
Observability stack experience.

Responsibilities

Enable effective use of approved AI-assisted development tools such as enterprise copilots, internal GenAI tooling, and approved LLM platforms.
Develop and maintain reusable prompt patterns, agent workflows, and repository-level guidance to standardize AI-assisted development across teams.
Partner with internal AI governance and architecture groups to pre-align use cases, reduce approval friction, and avoid rework.
Apply AI-assisted techniques to analyze legacy codebases, extract business rules, and document undocumented behavior.
Support decomposition of monolithic applications into domain-aligned, API-first architectures using AI-driven dependency analysis.
Assist teams modernizing legacy stacks including mainframe, batch, and large Java-based systems.
Enable AI-assisted generation of unit, integration, contract, and regression tests.
Help integrate AI-generated tests into existing CI/CD pipelines without compromising quality, compliance, or auditability.
Improve test coverage and reduce manual testing toil across modernization programs.
Act as a hands-on coach for engineers and tech leads on compliant, effective AI usage.
Create lightweight training materials, examples, and playbooks tailored to real delivery scenarios.
Embed with teams temporarily to drive adoption, not just documentation.
Ensure AI usage aligns with security, data privacy, PCI, and internal risk requirements.
Work closely with architecture, security, and risk partners early in the lifecycle to avoid late-stage blockers.