Java + Native AI

Role overview

Java + Native AI at Zensar

Preferred qualifications and skills

• Experience in Insurance domain modernization projects is highly desirable.

Employment details

• Location: Remote (Pune preferred).
• Prior experience presenting POC outcomes, ROI metrics, and AI transformation recommendations to leadership teams will be an added advantage.
• Strong understanding of GenAI technologies, including LLMs, prompt engineering, AI-assisted software development, code modernization accelerators, GitHub Copilot, Azure OpenAI/OpenAI services, and AI-enabled developer productivity tools. 8+ years of hands-on experience in Java application development with strong expertise in Java, Spring Boot, Microservices, REST APIs, enterprise integration, and modernization of large-scale legacy applications.
• Prior experience presenting POC outcomes, ROI metrics, and AI transformation recommendations to leadership teams will be an added advantage. .
• Location: Pune [Maharashtra] [India]

Application details

• 8+ years of hands-on experience in Java application development with strong expertise in Java, Spring Boot, Microservices, REST APIs, enterprise integration, and modernization of large-scale legacy applications.

Benefits

• Responsible for driving AI/Agentic AI Proof of Concepts (POCs) to identify productivity gains, automate development and testing activities, accelerate code modernization, improve documentation generation, and establish measurable business benefits.

Additional role details

• Proven experience leading or contributing to Java modernization initiatives, including Java version upgrades, monolith-to-microservices transformation, application refactoring, middleware migration (e.g., WebLogic to Tomcat), performance optimization, and cloud readiness assessments.
• Strong understanding of GenAI technologies, including LLMs, prompt engineering, AI-assisted software development, code modernization accelerators, GitHub Copilot, Azure OpenAI/OpenAI services, and AI-enabled developer productivity tools.
• Hands-on experience with Agentic AI concepts and frameworks, including AI agents, autonomous workflows, multi-agent orchestration, MCP, LangChain, CrewAI, AutoGen, semantic search, RAG, and AI-driven software engineering use cases.
• Strong analytical and communication skills with the ability to collaborate with architects, business stakeholders, QA teams, and engineering leadership to define modernization roadmaps and AI adoption strategies.