AI Architect with Enterprise Java , Gen AI and AI Powered Tools

AI Architect – Enterprise Java, GenAI & AI-Powered Engineering (Onsite)

Experience: 12–15 Years
AI/GenAI Experience: 2–3+ Years (Hands-on)
Work Model: Hybrid /3 Days a week 

Role Overview

We are seeking a highly experienced AI Architect with strong expertise in Enterprise Java, GenAI, Agentic AI, and AI-powered software engineering. This is a hands-on onsite role, requiring close collaboration with customers, business stakeholders, and engineering teams to drive AI-led transformation initiatives.

The ideal candidate will combine deep experience in Java enterprise architecture with practical knowledge of LLMs, RAG, AI Agents, and GenAI frameworks, while leveraging AI-powered engineering tools such as Windsurf, Cursor, GitHub Copilot, Claude Code, or equivalent to accelerate software development and modernization.

The role demands active participation in customer workshops, architecture discussions, solution design, PoCs, technical reviews, and mentoring teams, with regular onsite interaction to understand business challenges and deliver practical AI solutions.

Key Responsibilities

AI & GenAI Architecture

• Design and implement enterprise-scale GenAI, RAG, and Agentic AI solutions.
• Lead engagements from discovery and architecture through PoC, MVP, and production rollout.
• Evaluate LLMs, AI frameworks, vector databases, and cloud AI services.
• Integrate AI capabilities into enterprise applications and business processes.

Enterprise Java Architecture

• Provide technical leadership for Java, Spring Boot, REST APIs, Microservices, and event-driven architectures.
• Support modernization of legacy applications using cloud-native and AI-assisted approaches.
• Review code, establish architecture standards, and guide engineering teams.

AI-Powered Engineering

• Demonstrate practical use of AI development tools including Windsurf, Cursor, GitHub Copilot, and Claude Code.
• Drive AI adoption across the SDLC: requirements, development, testing, code review, documentation, and modernization.
• Measure and improve engineering productivity using AI-enabled practices.

Customer Engagement

• Conduct onsite customer workshops, discovery sessions, and architecture discussions.
• Translate business requirements into scalable AI and technology solutions.
• Support proposals, solutioning, estimations, and executive presentations.

Innovation & Leadership

• Mentor architects and engineering teams on Java, GenAI, and AI-assisted development.
• Participate in hackathons and innovation initiatives.
• Build reusable accelerators, frameworks, and AI assets.

Mandatory Skills

Enterprise Java

• 12–15 years of experience in software engineering and architecture.
• Strong hands-on expertise in:
• Java 11/17+
• Spring Boot
• REST APIs
• Microservices
• Distributed Systems
• Event-Driven Architecture
• SQL Databases

GenAI & Agentic AI

• 2–3+ years of hands-on experience with:
• LLMs
• RAG
• Prompt Engineering
• Embeddings & Vector Databases
• AI Agents / Agentic AI
• Function Calling

AI Frameworks

Experience with one or more:

• LangChain
• LangGraph
• Semantic Kernel
• LlamaIndex
• CrewAI
• AutoGen

AI Development Tools

Hands-on experience with:

• Windsurf
• Cursor
• GitHub Copilot
• Claude Code
• OpenAI Codex
• Equivalent Agentic IDEs

Cloud & Platforms

• Azure / AWS / Google Cloud Platform
• Docker & Kubernetes
• CI/CD & DevOps
• AI Security, Responsible AI, LLMOps, Guardrails, and Observability

Preferred Profile

• Senior Java Architect with GenAI experience
• Principal Engineer with Java + AI background
• Enterprise Architect with hands-on coding expertise
• AI Architect with strong enterprise application development experience

Key Selection Criteria

The ideal candidate should be:

• Strong in Enterprise Java Architecture
• Hands-on with RAG, LLMs, and Agentic AI
• Experienced in AI-assisted software engineering
• Customer-facing and consultative
• Innovation-driven and hackathon-oriented
• Comfortable working in an onsite customer environment

Note: This Role requires regular interaction with customer teams, architects, business stakeholders, and delivery teams to drive successful AI transformation initiatives.