We are seeking an experienced Java Solution Architect to design, define, and govern scalable, secure, and cloud-native enterprise architectures.
The ideal candidate will have strong expertise in Java and Spring technologies, proven architectural leadership, and hands-on experience with AI-Native architecture patterns, including RAG, LLMs, Agentic AI frameworks, and cloud-based AI services.
The role will involve working closely with engineering, QE, DevOps, and client teams to drive technical strategy, architecture standards, and successful delivery of enterprise solutions.
Key Responsibilities
Design and own scalable, resilient, and cloud-native application architectures for enterprise Java platforms.
Define and document High-Level Design (HLD) and Low-Level Design (LLD) for complex systems and applications.
Lead architecture reviews, technical design discussions, and code reviews across development squads.
Establish and enforce coding standards, architectural best practices, design patterns, and engineering principles.
Provide technical leadership, mentorship, and guidance to development and QE teams.
Collaborate with onshore client and partner teams to understand requirements, align priorities, and drive project milestones.
Design and implement AI-Native architectures leveraging:
Retrieval-Augmented Generation (RAG)
Embeddings and vector-based retrieval
LLM integrations and deployments
Agentic AI frameworks
AI application memory management strategies
Leverage Azure AI and Azure OpenAI services for enterprise GenAI solutions and deployment.
Define strategies for memory management, context handling, and knowledge retrieval in AI-powered applications.
Drive DevOps automation, CI/CD, and deployment practices across application teams.
Work closely with security, infrastructure, and platform teams to ensure solutions meet enterprise security, scalability, reliability, and compliance requirements.
Provide technical direction across multiple squads and contribute to organization-wide architecture initiatives.
Required Skills & Experience
9+ years of overall software engineering experience, with 13+ years preferred.
3–5+ years of experience in a Solution/Technical Architect role, designing both on-premises and cloud-based systems.
Expert-level knowledge of:
Java
Spring Boot
REST APIs
Spring Batch
Strong experience in microservices architecture, distributed systems, and enterprise application design.
3+ years of hands-on experience with AI-Native architecture, including:
RAG
LLMs
Embeddings
Agentic frameworks
GenAI integrations
AI application memory management
Cloud AI services
Hands-on experience with Azure OpenAI, GenAI solution development, and LLM/model fine-tuning or customization.
Strong understanding of HLD/LLD, architecture patterns, design principles, and technical documentation.
Experience with DevOps and engineering tools, including Maven, Git, CI/CD, and Autosys.
Good understanding of cloud architecture, security, scalability, and deployment models.
Good to have experience with RPM packaging and Linux environments.
Excellent communication and stakeholder-management skills, with the ability to interact effectively with both technical and non-technical stakeholders.
Ability to act as a Subject Matter Expert (SME) across multiple IS disciplines, including business solutions, systems development, security, and technology architecture.
Preferred Profile
Candidates with strong AI-Native Solution Architecture experience will be highly preferred, particularly those who have hands-on expertise in RAG, LLMs, Agentic AI frameworks, Azure OpenAI, embeddings, and enterprise GenAI solutions.
The ideal candidate should combine deep Java architecture expertise with modern AI and cloud capabilities and be comfortable driving technical decisions across large, complex enterprise environments.
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