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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.
Job Responsibility:
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
Develop RAG pipelines and LLM-based reasoning components using Java to deliver accurate, context-aware insights grounded in insurance documentation and structured data
Build and optimize secure, high-performance RESTful APIs in Java/Spring Boot to interface with generative models, knowledge bases, and insurance core systems
Containerize and deploy AI workloads on Microsoft Azure, leveraging Azure OpenAI Service, Azure ML, and App Services for robust scalability, monitoring, and compliance
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:
Advanced proficiency in Java, with strong understanding of concurrency, memory management, and JVM optimization
Experience with LangChain4j, Spring AI, Spring Batch, Quarkus for AI application development
Demonstrated experience with Gen AI, LLM integration, and Agentic architectures
Familiarity with Azure AI ecosystem (Azure ML, Cognitive Search, OpenAI Service)
Proficiency in API design, unit testing, and CI/CD pipelines using tools such as Maven, Gradle, or GitHub Actions
Strong communication skills and ability to produce technical design documents and architecture diagrams
Nice to have:
Experience developing AI orchestration pipelines or knowledge graph–driven 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