SENIOR TECHNICAL LEAD - Gen AI

AI Architect - Enterprise Java, Gen AI & AI-Powered Engineering
Experience: 12-15 years overall
AI/Gen AI Experience: 2-3+ years of hands-on experience
Location: Hyderabad
Role Overview
We are looking for an AI Architect who combines deep enterprise Java expertise with strong hands-on AI/Gen AI 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 Gen AI, 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.
Key Responsibilities
1. AI/Gen AI Solution Architecture
Work with customers and business stakeholders to understand business problems and identify opportunities for AI/Gen AI adoption.
Design scalable and production-ready Gen AI 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
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.
2. Enterprise Java Architecture
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.
3. AI-Powered Software Engineering
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
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.
4. GenAI / Agentic AI Development
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.
5. Customer Engagement
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.
6. Technical Leadership
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.
7. Innovation & Hackathons
Actively participate in AI/Gen AI hackathons and innovation initiatives.
Build rapid prototypes using Gen AI , 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.
Required Technical Skills
Java & Enterprise Architecture - Mandatory
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
Strong understanding of enterprise application architecture.
Experience with application modernization and cloud-native architectures.
Ability to review and contribute to production-quality Java code.
GenAI / AI - Mandatory
2-3+ years of practical hands-on Gen AI/LLM experience.
Strong understanding of:
LLMs
RAG
Prompt engineering
Embeddings
Vector search
AI Agents / Agentic AI
Function/tool calling
Hands-on experience building working Gen AI PoCs or production solutions.
Experience with at least one major AI ecosystem such as:
Azure OpenAI / Microsoft AI Foundry
AWS Bedrock
Google Vertex AI
AI Frameworks
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.
AI-Powered Development Tools ? Mandatory
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.
Cloud & Engineering
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.
AI Governance & Production Readiness
Understanding of:
AI security
Data privacy
Responsible AI
LLM evaluation
AI observability
Guardrails
Cost optimization
LLMOps / AI application operations
Preferred Qualifications
E

Work arrangement
No