ARCHITECT - Java

Experience: 12?15 years overall AI/GenAI Experience: 2?3+ years of hands-on experience Location: [Location] Employment Type: Full-time

We are looking for an AI Architect who combines deep enterprise Java expertise with strong hands-on AI/GenAI capabilities and can leverage modern AI-powered development tools to significantly accelerate software engineering productivity.

The ideal candidate will have a strong background in Java enterprise application development and architecture , combined with practical experience building GenAI, RAG, and Agentic AI solutions .

This is a hands-on architecture role . The candidate should be comfortable moving between customer discussions, architecture design, coding, PoCs, technical reviews, and mentoring engineering teams.

The candidate will also be expected to demonstrate how tools such as Windsurf, Cursor, GitHub Copilot, Claude Code, or equivalent AI-powered development tools can be used across the software development lifecycle to improve developer productivity, accelerate modernization, and reduce development effort.

Work with customers and business stakeholders to understand business problems and identify opportunities for AI/GenAI adoption .

Design scalable and production-ready GenAI and Agentic AI solutions .

Lead solutions from problem definition ? architecture ? PoC ? MVP ? production .

Design and implement solutions using: LLMs RAG AI Agents / Agentic AI Embeddings Vector databases Prompt engineering Function/tool calling AI orchestration

LLMs

RAG

AI Agents / Agentic AI

Embeddings

Vector databases

Prompt engineering

Function/tool calling

AI orchestration

Evaluate different LLMs, AI frameworks, cloud services, and technology options based on business requirements.

Define appropriate architecture patterns for integrating AI capabilities with enterprise applications, APIs, databases, and business processes.

Provide strong technical leadership for Java-based enterprise applications .

Design and develop applications using Java, Spring Boot, REST APIs, Microservices and event-driven architectures .

Integrate GenAI capabilities into existing Java applications and enterprise platforms.

Review and contribute to Java code where required, particularly during PoCs, critical technical implementations, and architecture validation.

Define application architecture, integration patterns, coding standards, and technical guidelines.

Support legacy Java application modernization using cloud, microservices, and AI-assisted engineering approaches.

Guide engineering teams on scalability, performance, resiliency, security, and maintainability.

Use AI-powered development and Agentic IDE tools such as Windsurf, Cursor, GitHub Copilot, Claude Code, or equivalent tools.

Demonstrate practical usage of these tools for: Code generation Code understanding Refactoring Test generation Debugging Documentation API development Application modernization Code migration Rapid PoC development

Code generation

Code understanding

Refactoring

Test generation

Debugging

Documentation

API development

Application modernization

Code migration

Rapid PoC development

Identify opportunities to use AI across the software development lifecycle , including requirements, design, development, testing, code review, documentation, and maintenance.

Establish practical guidelines for using AI coding tools while maintaining security, architecture standards, code quality, and maintainability .

Help development teams adopt AI-assisted engineering practices and measure improvements in developer productivity and delivery efficiency .

This emphasis reflects how current market roles are moving beyond simply "knowing Copilot" toward using AI throughout the SDLC.

Build and demonstrate working AI/GenAI prototypes.

Develop RAG pipelines using enterprise data sources.

Design agentic workflows involving agents, tools, APIs, memory/state, and human-in-the-loop controls .

Work with frameworks such as LangChain, LangGraph, Semantic Kernel, LlamaIndex, CrewAI, AutoGen, or equivalent .

Implement appropriate evaluation, observability, guardrails, security, and monitoring mechanisms.

Understand when to use RAG, traditional software, workflow automation, or Agentic AI based on the business problem.

Participate in customer discovery sessions, workshops, architecture discussions, and technical presentations.

Act as a technical advisor for AI-led transformation initiatives.

Translate business problems into practical technology and AI solutions.

Conduct GenAI assessments and identify opportunities for AI-led modernization and automation.

Present architecture, PoCs, technology recommendations, and implementation roadmaps to technical and business stakeholders.

Support solutioning, proposals, RFPs, technical estimations, and customer presentations where required.

Provide technical direction to Java, AI, cloud, and engineering teams.

Mentor architects, technical leads, and developers on Java, GenAI, Agentic AI, and AI-assisted engineering .

Review architecture, code, technical designs, and implementation approaches.

Identify technical risks and define mitigation approaches.

Establish reusable architecture patterns and accelerators.

Collaborate with cloud, DevOps, security, data, QA, and product teams.

Actively participate in AI/GenAI hackathons and innovation initiatives .

Build rapid prototypes using GenAI, Agentic AI, Java, cloud platforms, and AI-powered development tools.

Experiment with emerging AI technologies and identify practical enterprise applications.

Convert successful experiments into reusable PoCs, accelerators, or production use cases.

12?15 years of overall software engineering/architecture experience .

Strong hands-on experience with Java .

Strong experience with: Java 11/17+ Spring Boot Spring MVC / Spring Security REST APIs Microservices Distributed systems Event-driven architecture SQL/relational databases

Java 11/17+

Spring Boot

Spring MVC / Spring Security

REST APIs

Microservices

Distributed systems

Event-driven architecture

SQL/relational databases

Strong understanding of enterprise application architecture.

Experience with application modernization and cloud-native architectures.

Ability to review and contribute to production-quality Java code.

2?3+ years of practical hands-on GenAI/LLM experience .

Strong understanding of: LLMs RAG Prompt engineering Embeddings Vector search AI Agents / Agentic AI Function/tool calling

Vector search

Hands-on experience building working GenAI PoCs or production solutions .

Experience with at least one major AI ecosystem such as: Azure OpenAI / Microsoft AI Foundry AWS Bedrock Google Vertex AI

Azure OpenAI / Microsoft AI Foundry

AWS Bedrock

Google Vertex AI

Hands-on experience with one or more of:

LangChain

LangGraph

Semantic Kernel

LlamaIndex

CrewAI

AutoGen

Equivalent GenAI/Agentic AI frameworks

The candidate does not need expertise in every framework.

Hands-on experience with one or more AI-powered development tools such as:

Windsurf

Cursor

GitHub Copilot

Claude Code

OpenAI Codex

Equivalent Agentic IDE/development tools

The candidate should be able to demonstrate practical use of these tools for real software engineering activities , not merely code completion.

Strong understanding of cloud-native application architecture.

Experience with Azure, AWS, or GCP.

Knowledge of Docker and Kubernetes.

Understanding of CI/CD and DevOps practices.

Understanding of application security, authentication, authorization, APIs, and enterprise integration.

Understanding of: AI security Data privacy Responsible AI LLM evaluation AI observability Guardrails Cost optimization LLMOps / AI application operations

AI security

Data privacy

Responsible AI

LLM evaluation

AI observability

Guardrails

Cost optimization

LLMOps / AI application operations

The ideal candidate should be:

1. A strong Java architect Deep understanding of enterprise Java and capable of reviewing and guiding real implementation.

2. Hands-on with GenAI Has actually built RAG/LLM/Agentic solutions rather than only having theoretical knowledge.

3. An AI-enabled engineer Uses tools such as Windsurf, Cursor, Copilot, Claude Code, or equivalent to accelerate real development work.

4. An enterprise architect Understands APIs, microservices, cloud, security, integration, scalability, and production engineering.

5. Customer-facing Can independently conduct discovery, architecture discussions, workshops, and technical presentations.

6. Innovation-oriented Willing to experiment, participate in hackathons, and convert new AI capabilities into practical enterprise solutions.

A realistic candidate for this role could come from backgrounds such as:

Senior Java Architect / Solution Architect who has moved into GenAI

Principal Engineer with strong Java + GenAI experience

Java Technical Architect / Engineering Architect with 2?3+ years of GenAI work

Enterprise Architect who is still hands-on with Java and has built GenAI PoCs

AI/GenAI Architect with a strong Java application-development background

The target profile is not necessarily a traditional ML researcher or data scientist . The focus is on someone who can combine enterprise software engineering + AI application development + architecture .

This is consistent with current market roles: for example, Persistent is currently advertising a 12?15-year Java GenAI Architect profile combining deep Java, Spring Boot, microservices, architecture, RAG, and AI-assisted development tools; Capgemini similarly combines Java, hands-on coding, GenAI, RAG, and AI across the SDLC

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