Uses Python as needed for AI/agent prototyping, automation, evaluation harnesses, or glue code/integrations
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
Required qualifications, capabilities, and skills
Formal training or certification on software engineering concepts and 3+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced Java development experience with strong fundamentals (OO design, concurrency, performance, debugging)
Strong hands-on experience with Spring Boot and related frameworks (REST APIs, security, persistence), Elastic search, plus unit/integration testing.
AI4Tech hands-on experience using Copilot/Claude Code (or similar approved tools) to accelerate delivery while maintaining code quality, security, and test coverage
Experience building AI agents / LLM-enabled workflows, including prompt discipline, grounding/verification strategies, and safe handling of sensitive data
Some experience or working knowledge of Python (scripting, automation, or AI/agent prototyping)
Advanced understanding of agile methodologies such as CI/CD, Application Resiliency, and Security
Practical experience on Kubernetes
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
Preferred qualifications, capabilities, and skills
Experience designing and operating production-grade agentic systems (observability, evals, prompt/versioning, fallbacks, rate limits, and guardrails)
Experience with modern microservice patterns (resiliency, distributed tracing, event-driven design)
