Java Solution Architect (AI Native Architecture)
Role Overview
We are looking for an experienced Java Solution Architect to design and drive scalable, cloud-native enterprise solutions. The ideal candidate will have deep expertise in Java technologies, strong architectural leadership, and hands-on experience building AI-native applications using RAG, Large Language Models (LLMs), Agentic AI frameworks, and Azure AI services.
This role requires close collaboration with cross-functional teams, mentoring engineering teams, and defining technical roadmaps while ensuring best practices in architecture, development, and deployment.
Key Responsibilities
- Design and architect scalable, secure, and high-performance Java-based enterprise applications and microservices.
- Create and maintain High-Level Design (HLD) and Low-Level Design (LLD) documentation.
- Lead architecture discussions, design reviews, and technical governance across engineering teams.
- Provide technical leadership and mentor developers and QA engineers on architecture, coding standards, and best practices.
- Collaborate with client-facing and onshore teams to understand business requirements and deliver technical solutions.
- Design and implement AI-native architectures leveraging Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Agentic AI frameworks, and intelligent orchestration patterns.
- Integrate enterprise applications with Azure AI services, including Azure OpenAI, for Generative AI use cases.
- Build scalable AI solutions with effective context and memory management strategies.
- Conduct code reviews and ensure adherence to quality, security, and architectural standards.
- Drive DevOps practices, CI/CD automation, and cloud deployment strategies.
Required Skills & Experience
- 9+ years of software development experience (13+ years preferred).
- 3–5 years of experience as a Solution/Technical Architect designing enterprise applications across on-premises and cloud environments.
- Strong expertise in Java, Spring Boot, REST APIs, Microservices, and Spring Batch.
- Hands-on experience with AI-native architecture, including:
- Retrieval-Augmented Generation (RAG)
- Large Language Models (LLMs)
- Agentic AI frameworks
- AI memory management
- Cloud AI services
- Experience integrating Azure OpenAI and building Generative AI solutions.
- Strong understanding of software architecture principles, design patterns, scalability, and distributed systems.
- Experience with Maven, Git, CI/CD pipelines, and DevOps practices.
- Strong knowledge of High-Level Design (HLD) and Low-Level Design (LLD).
- Excellent communication and stakeholder management skills.
Good to Have
- Experience with RPM Packaging.
- Linux administration and deployment experience.
- Knowledge of enterprise security and cloud architecture best practices.
Preferred Candidate Profile
