Java Application Engineer with AI & Google Cloud Platform

Role : Java Application Engineer with AI & Google Cloud Platform

Location : Remote

Experience : 12+ Years

Skills : Java, AI, Google Cloud Platform, Spring Boot, Spring Batch, Angular, GKE/Kubernetes, CI/CD, Claude, SQL, PostgreSQL, Alloy DB

Key Skills & Experience :

• Strong Java development experience, including Spring Boot, Spring Batch (required), and full-stack development (Angular preferred)
• Experience using AI tools, including prompt engineering and Claude for software development and modernization activities
• Strong database expertise, including SQL, data modeling, and PostgreSQL
• Experience with Google Cloud Platform (Google Cloud Platform) and Google Kubernetes Engine (GKE)
• Proven application modernization experience
• Knowledge of legacy mainframe technologies, including COBOL, JCL, and VSAM, is highly desirable

Key Responsibilities :

• Utilize AI-assisted development tools to analyze legacy applications and support modernization efforts
• Design and optimize prompts to improve business rule extraction and code conversion outcomes
• Develop and validate modernized Java applications using Spring Boot and Spring Batch
• Review and refine AI-generated code to ensure quality, compliance, and functional accuracy
• Support migration of VSAM-based data structures to Alloy DB
• Build, deploy, and support containerized applications within Google Cloud Platform/GKE environments
• Develop and optimize SQL solutions, database designs, and performance tuning strategies
• Collaborate with architects, SMEs, business analysts, and modernization teams to achieve delivery objectives

Desired Technical Background :

• Java, Spring Boot, Spring Batch
• Google Cloud Platform, GKE/Kubernetes
• CI/CD and DevOps practices
• Claude or similar AI-assisted development tools
• Prompt engineering and AI-enabled software development
• SQL, PostgreSQL, Alloy DB
• Mainframe modernization experience

Expected Outcomes :

• Accelerated and high-quality modernization of legacy applications
• Accurate extraction and documentation of business requirements and business rules
• Successful migration of legacy data assets to modern database platforms
• Stable deployment and operation within cloud-native environments
• Continuous improvement in AI-assisted development productivity and delivery quality