AI-Enhanced Development & SAP Connectivity: Design, develop, and maintain high-performance, scalable Java microservices (primarily using Spring Boot/Spring Framework). Utilize AI coding assistants (e.g., GitHub Copilot, Claude Code, internal tools) to increase speed and offload repetitive tasks.
Critical AI Code Review & Optimization: Serve as the final technical authority on all AI-generated code. Rigorously review, debug, and optimize suggested code for performance, security, and adherence to SAP Clean Core standards and enterprise design patterns.
System Architecture & Hybrid SAP Connectivity: Lead the design and implementation of distributed connectivity components across hybrid SAP ECC and S/4HANA estates, ensuring resilience, low latency, and clean data structures.
Prompt Engineering for SDLC: Apply advanced prompt engineering techniques to maximize the efficacy of generative AI tools for writing comprehensive unit/integration tests, drafting technical documentation (Javadoc, API specs), and generating code refactoring suggestions.
CI/CD and Observability: Oversee and enhance automated build, test, and deployment pipelines (CI/CD, Kubernetes/Docker) in a cloud environment (AWS/Azure/GCP), leveraging AI tools for proactive anomaly detection and monitoring.
- Required Technical Qualifications
- 5+ years of professional experience in developing enterprise-level applications using Java (8+ / modern Java).
- Proficiency with the Spring Ecosystem (Spring Boot, Spring Data, Spring Security).
- Deep understanding of Object-Oriented Design (OOD), software design patterns, and Microservices architecture.
- SAP Connectivity & Clean Core Architecture: Proven experience building with SAP Clean Core principles across both SAP ECC and S/4HANA environments.
SAP Basis & Technical Integration Literacy: Familiarity with SAP Basis administration concepts, system architecture, RFC connections, secure gateways, and system integration troubleshooting.
- Strong practical experience with concurrency and multithreading in Java to build highly performant services.
- Proficiency in modern DevOps practices, including Git, Docker, Kubernetes, and CI/CD pipelines (e.g., Jenkins, GitLab CI).
- Solid understanding of front-end technologies such as HTML, CSS, JavaScript, TypeScript, and modern JavaScript frameworks (e.g., ReactJS, MobX).
- Experience with relational (PostgreSQL, MySQL) and NoSQL databases (MongoDB, Redis).
- Essential AI-Era Skills (The "New Bar")
- AI Tool Fluency: Demonstrated ability to proficiently use AI-powered developer tools (e.g., GitHub Copilot, CodeWhisperer) in a production environment.
Code Judgment: Proven track record of evaluating and overriding AI-generated suggestions that are technically correct but architecturally flawed or non-compliant with security and performance best practices.
Prompting Proficiency: Experience writing clear, contextual, and effective prompts for generative AI to produce usable code, tests, or documentation.
Adaptability & Learning: High degree of curiosity and a proactive approach to learning new AI tools and techniques that continuously reshape the development workflow.
Domain & Architectural Focus: The capacity to abstract away from low-level coding details to focus on high-value business logic and systemic architectural integrity.
Cloud-First Mentality (AWS): A strong bias towards cloud-first development, with specific expertise in architecture and deployment on Amazon Web Services (AWS).