Back-end Software Development Engineer #1061590
Job Description:
Employees in this job function develop and maintain the back-end/ server-side parts of an application, typically consisting of APIs, databases and other services containing business logic. They work with various languages and tools to create and maintain services on-prem or in the cloud.
Key Responsibilities:

• Engage with customers to understand their use-cases and requirements
• Solve complex problems by designing, developing, and delivering using various tools, languages, frameworks, and technologies
• Align with architecture guidelines for unified and coherent approach to development
• Design, develop, and deliver new code using various tools, languages, frameworks, and technologies
• Develop and maintain back-end applications like APIs and microservices using server-side languages like Java, Python, C#, etc.
• Collaborate with front-end developers to integrate user interface elements and with cross functional teams like product owners, designers, architects etc.
• Manage application deployment to the cloud or on-prem, health and performance monitoring, security hardening and disaster recovery for deployed applications
• Manage data storage and retrievals in applications by utilizing database technologies such as Oracle, MySQL, MongoDB, etc.
• Promote improvements in programming practices, such as test-driven development, continuous integration, and continuous delivery
• Optimize back-end infrastructure and deployment practices to improve application resiliency and reliability
• Support security practices to safeguard user data including encryption and anonymization

Skills Required:
Java, Spring Boot, AI, PostgreSQL
Skills Preferred:

• GCP, Pub/Sub, SonarQube
• Experience: 5+ years of professional backend software engineering experience.
• Core Tech: Strong expertise in Java and the Spring Boot ecosystem, with proven experience building and maintaining production services.
• AI Tooling (Hands-On): Practical, day-to-day experience with AI-assisted development tools such as GitHub Copilot, LLMs (e.g., ChatGPT, Claude, Gemini), and notebooks (Jupyter or similar) applied to real engineering work.
• Code Comprehension: Demonstrated ability to quickly understand and navigate large, complex, or legacy codebases.
• APIs & Databases: Solid experience with RESTful APIs, microservices, and relational databases (SQL, schema design, query optimization).
• Engineering Rigor: Strong grasp of testing, Git, CI/CD, and clean code principles—plus the discipline to validate and verify AI-generated code.
• Critical Thinking: The judgment to know when AI output is trustworthy, when it needs verification, and when to rely on first-principles engineering.

Experience Required:

• Engineer 3
• Exp: Proficient In 2 coding lang. or adv. Prac. in 1 lang.
• 6+ years in IT;
• 4+ years in development

Experience Preferred:

• Prompt Engineering: Experience crafting effective prompts and workflows for code understanding, documentation, and generation.
• LLM Integration: Exposure to building applications that integrate LLMs (e.g., RAG pipelines, embeddings, vector databases, LangChain, or similar frameworks).
• Python: Familiarity with Python for scripting, notebooks, and AI/ML tooling.
• Cloud: Experience with cloud platforms (GCP, AWS, or Azure) and their AI/ML services.
• Legacy Modernization: Prior experience reverse-engineering or modernizing legacy systems.
• Documentation Tooling: Experience generating architecture diagrams, API docs, or knowledge bases (including AI-assisted approaches).

Education Required:
Bachelor's Degree
Additional Info:

• Understand Complex Systems Fast: Use AI-assisted techniques (Copilot, LLMs, code-analysis notebooks) to explore, map, and comprehend large existing applications, services, and their interdependencies.
• Build & Maintain Backend Services: Design, develop, and optimize robust microservices and APIs using Java and Spring Boot.
• Reverse-Engineer & Document Legacy Logic: Analyze undocumented or legacy code, extract business logic, and produce clear technical documentation, diagrams, and knowledge artifacts—leveraging LLMs to accelerate the process.
• Apply AI to Engineering Workflows: Use LLMs and notebooks for code comprehension, test generation, refactoring assistance, dependency analysis, and impact assessment. Establish good practices for prompt design, validation, and safe use of AI-generated output.
• Improve Developer Productivity: Champion AI-assisted development practices across the team, sharing patterns, tooling, and guardrails that help engineers work faster without compromising quality.
• Ensure Quality & Correctness: Write comprehensive tests and rigorously validate AI-assisted output—treating AI as an accelerator, never a substitute for engineering judgment.
• Collaborate: Work closely with architects, product managers, and fellow engineers to translate legacy understanding into modernization and enhancement plans.