AI Engineer with Java

NTT DATA BUSINESS SOLUTIONS; Bengaluru; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

NTT DATA BUSINESS SOLUTIONS is seeking a highly skilled AI Engineer to develop AI use cases using Gen AI and Agentic AI tech. You will leverage Java and JVM optimizations, with frameworks like Spring AI, LangChain4j, and Quarkus to design autonomous insurance workflows.

Work involves Azure OpenAI Service, Azure ML, and scalable JVM architectures to deliver secure, compliant AI deployments while collaborating with data engineers and business analysts.

Qualifications

• Strong proficiency in Java with focus on concurrency and memory management.
• Experience with Gen AI, LLM integration, and Agentic architectures.
• Hands-on in JVM-based AI app development using LangChain4j, Spring AI, Spring Batch, Quarkus.
• Familiarity with Azure AI services and API design, testing, and CI/CD pipelines.

Responsibilities

• Design, implement, and orchestrate Agentic AI systems for insurance processes.
• Develop RAG pipelines and LLM-based reasoning in Java for contextual insights.
• Build secure, high-performance RESTful services with Java/Spring Boot.
• Containerize workloads for Azure deployment with monitoring and compliance.
• Collaborate with data engineers and architects to embed AI in existing ecosystems.

Skills

Java

JVM optimization

Gen AI

LangChain4j

Spring AI

Spring Batch

Quarkus

Azure AI

API design

CI/CD

Tools

LangChain4j

Spring AI

Spring Batch

Quarkus

Maven

Gradle

GitHub Actions

Azure OpenAI Service

Azure ML

Job description

Job Summary

Job Description: A highly skilled AI Engineer to develop AI use cases using Gen AI and Agentic AI technologies. The ideal candidate will have deep expertise in Java, enterprise-grade frameworks, and next-generation AI integrations using platforms such as Spring AI, Spring Batch, and Quarkus. This role focuses on designing intelligent, autonomous systems that transform insurance workflows, leveraging Azure-based AI services and scalable Java architectures.

Responsibilities

• AI Agentic System Development: Design, implement, and orchestrate Agentic AI systems using LangChain4j, Spring AI, and other JVM-based frameworks to enable autonomous reasoning, decision-making, and task execution across insurance processes such as claims triage, underwriting, and customer service.
• Generative AI Integration: Develop RAG pipelines and LLM-based reasoning components using Java to deliver accurate, context-aware insights grounded in insurance documentation and structured data.
• API Service Engineering: Build and optimize secure, high-performance RESTful APIs in Java/Spring Boot to interface with generative models, knowledge bases, and insurance core systems.
• Cloud-Native AI Deployment: Containerize and deploy AI workloads on Microsoft Azure, leveraging Azure OpenAI Service, Azure ML, and App Services for robust scalability, monitoring, and compliance.
• System Integration Collaboration: Work closely with data engineers, solution architects, and business analysts to embed AI capabilities within existing insurance ecosystems integrating policy data, claims systems etc.

Requirements

• Languages Tools: Advanced proficiency in Java, with strong understanding of concurrency, memory management, and JVM optimization.
• Frameworks: Experience with LangChain4j, Spring AI, Spring Batch, Quarkus for AI application development.
• AI Expertise: Demonstrated experience with Gen AI, LLM integration, and Agentic architectures.
• Cloud Knowledge: Familiarity with Azure AI ecosystem (Azure ML, Cognitive Search, OpenAI Service).
• Development Practices: Proficiency in API design, unit testing, and CI/CD pipelines using tools such as Maven, Gradle, or GitHub Actions.
• Collaboration Documentation: Strong communication skills and ability to produce technical design documents and architecture diagrams.

Nice to Haves

• Experience developing AI orchestration pipelines or knowledge graphdriven retrieval systems.
• Familiarity with vector databases (e.g., Pinecone, FAISS, Chroma) and semantic search indexing.
• Experience fine-tuning or serving LLMs through Java-based inference layers.
• Exposure to AIOps practices (e.g., monitoring, retraining workflows).
• Understanding of core insurance platforms such as Guidewire, Duck Creek, or Sapiens.
• Interest in open-source AI development within the Java community.