Manager | Java Springboot | Bengaluru | Engineering as a Service/ Operate

Java Solution Architect

Role Summary

Java Solution Architect to define and govern scalable, cloud-native architectures for Edward Jones. The ideal candidate brings expert-level Java and Spring expertise, strong architectural leadership, and increasingly, hands-on experience with AI Native architecture patterns including RAG, LLMs, and cloud AI services.

Key Responsibilities

• Design and own scalable application architectures and microservices for enterprise Java systems
• Produce High-Level Design (HLD) and Low-Level Design (LLD) documentation
• Lead architecture reviews and ensure adherence to coding and architectural best practices
• Mentor and guide development and QE teams on technical decisions
• Coordinate with onshore (client/partner) teams to align on priorities, requirements, and milestones
• Design AI Native architectures including RAG-based embeddings, Agentic frameworks, and LLM integrations
• Work with Azure cloud AI services for AI integration and deployment
• Implement and manage memory management strategies for AI-powered applications
• Conduct and lead code reviews across squads
• Drive DevOps automation and deployment pipelines

Skills & Experience Required

• 9+ years of software engineering experience; 13+ years preferred
• 3–5 years as an architect on technical design for both on-premises and cloud systems
• Expert proficiency in Java, Spring Boot, REST APIs, and Spring Batch
• 3+ years of AI Native architecture experience — RAG, LLM deployment, memory management, cloud AI services
• Hands-on experience with Azure OpenAI, fine-tuning models, and GenAI integrations
• Strong skills in HLD and LLD, DevOps tooling (Maven, Git, CI/CD, Autosys)
• Good to have: RPM Packaging and Linux
• Excellent communication with both technical and non-technical stakeholders
• Subject matter expert across multiple IS disciplines — business solutions, systems development, security

Note: Candidates with AI Native architecture experience (RAG, LLMs, Agentic frameworks) are strongly preferred.