Java Solution Architect - AI solutions
Weekday AI · Bengaluru, Karnataka, India · ₹3M–₹4M/yr
Technology, Information and Internet · 11-50 employees
About the role
The architect will design and govern scalable, cloud-native enterprise applications while integrating AI-native solutions like LLMs and RAG. They are also responsible for mentoring engineering teams and ensuring technical standards across the software development lifecycle.
What they look for
Requirements
Candidates must have 12+ years of software engineering experience with deep expertise in Java, Spring Boot, and microservices architecture. Proven experience in designing AI-native systems and cloud-based deployments, specifically within Azure environments, is required.
Full description
𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀
𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟯𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟰𝟱𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟯𝟬-𝟰𝟱 𝗟𝗣𝗔)
Experience: 12+ yrs
Location: Bengaluru, Hyderabad
Job Type: Full-time
We are seeking an experienced Java Solution Architect to lead the design, development, and governance of scalable, cloud-native enterprise applications. This role is ideal for a seasoned technology leader with deep expertise in Java, Spring Boot, microservices, and modern architecture patterns, complemented by hands-on experience building AI-native solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and cloud-based AI services.
As a Java Solution Architect, you will define technical strategy, create scalable application architectures, and guide engineering teams through the complete software development lifecycle. You will collaborate with cross-functional stakeholders to design resilient, secure, and high-performing systems while driving architectural best practices, mentoring development teams, and enabling enterprise adoption of AI-powered capabilities. This role requires a strong balance of technical leadership, solution design expertise, and hands-on architectural execution across cloud and enterprise environments.
Key Responsibilities• Design and own scalable, secure, and cloud-native application architectures for enterprise Java-based platforms.
- Define microservices architecture, integration patterns, and technical roadmaps aligned with business objectives.
- Create and maintain High-Level Design (HLD) and Low-Level Design (LLD) documentation for enterprise solutions.
- Lead architecture reviews and ensure adherence to engineering standards, coding practices, and solution governance.
- Mentor development and quality engineering teams by providing architectural guidance and technical leadership.
- Collaborate with business stakeholders and cross-functional teams to align technical solutions with project priorities and delivery milestones.
- Design and implement AI-native architectures incorporating Retrieval-Augmented Generation (RAG), Large Language Models (LLMs), Agentic AI frameworks, and intelligent workflows.
- Integrate enterprise applications with cloud-based AI services while ensuring scalability, reliability, and security.
- Drive DevOps adoption by establishing CI/CD pipelines, deployment automation, and infrastructure best practices.
- Conduct code reviews, identify technical risks, and recommend performance, scalability, and reliability improvements across engineering teams.
What Makes You a Great Fit• 12+ years of software engineering experience with extensive expertise in enterprise application architecture and solution design.
- Strong hands-on experience designing enterprise systems using Java, Spring Boot, Spring Batch, REST APIs, and microservices architecture.
- Proven experience defining scalable cloud-native architectures across on-premises and cloud environments.
- Hands-on expertise in AI-native architecture, including RAG, LLMs, Agentic AI frameworks, prompt engineering, and cloud AI integration.
- Experience working with Azure cloud services, including Azure OpenAI and enterprise AI deployment strategies.
- Strong knowledge of distributed systems, application scalability, security, and performance optimization.
- Experience with DevOps tools, CI/CD pipelines, Git, Maven, deployment automation, and modern software delivery practices.
- Excellent architectural documentation skills, including HLD, LLD, technical governance, and solution reviews.
- Outstanding communication and stakeholder management skills with the ability to influence technical and business teams.
- A collaborative leadership style with a passion for mentoring engineers, driving innovation, and delivering enterprise-scale AI-enabled software solutions.
