Java Fullstack Developer + AI _D-2662
Allianz Insurance
Pune, Maharashtra, IndiaSENIOR
AI/ML engineeringJava fullstackBackend developmentInsurance domainLLM integrationMicroservicesRAG pipelinesVector databases
Job Description
We are seeking a highly skilled Senior Developer with strong hands-on experience in Java, Spring Boot, Kafka, and NoSQL databases — now expanded to include AI/ML engineering capabilities. The ideal candidate will design, develop, and maintain high-performance, scalable applications while integrating AI-powered features into enterprise insurance systems.
Responsibilities
- Design, develop, and enhance backend services and APIs using Java and Spring Boot.
- Build high-throughput and resilient event-driven components using Kafka.
- Work with MongoDB or other NoSQL databases to design efficient data models and optimize queries.
- Integrate Large Language Models (LLMs) such as OpenAI, Claude, or Gemini into backend services via REST APIs and SDKs.
- Build and maintain AI-powered microservices including RAG (Retrieval-Augmented Generation) pipelines, semantic search, and document intelligence features.
- Develop and expose AI agent workflows using frameworks such as LangChain4j, Spring AI, or similar Java-native AI toolkits.
- Implement prompt engineering strategies, context management, and output validation layers for LLM interactions.
- Design vector database integrations (Pinecone, Weaviate, pgvector) for embedding storage and similarity search.
- Collaborate closely with architects, BAs, and cross-functional teams to deliver robust technical solutions.
- Participate in code reviews, refactoring efforts, and optimisation of system performance.
- Ensure best practices for coding standards, security, maintainability, and AI model governance.
- Troubleshoot production issues and provide root cause analysis and long-term fixes.
- Support CI/CD pipeline integration, model deployment automation, and MLOps tooling.
- Contribute to documentation, technical specifications, and architectural diagrams.
Qualifications
- 5+ years of hands-on development experience in Java-based applications.
- Strong expertise in Java (8/11/17), Spring Framework, Spring Boot, and RESTful services.
- Practical knowledge of MongoDB or other NoSQL databases (e.g., Cassandra, DynamoDB, Couchbase).
- Solid understanding of microservices architecture and distributed systems.
- Hands-on experience consuming LLM APIs (OpenAI GPT-4o, Anthropic Claude, Google Gemini) in production Java applications.
- Familiarity with Spring AI or LangChain4j for building LLM-backed services in Java ecosystems.
- Knowledge of embedding models and vector stores for semantic search and RAG pipelines.
- Strong debugging, analytical, and problem-solving skills.
- Excellent communication and collaboration skills.
- Hands-on experience building or fine-tuning ML models using Python-based frameworks (Hugging Face, scikit-learn, PyTorch) integrated with Java services.
- Exposure to AI agent orchestration tools — LangGraph, AutoGen, CrewAI, or OpenAI Assistants API.
- Familiarity with MLOps platforms (MLflow, SageMaker, Kubeflow) for experiment tracking and model lifecycle management.
- Knowledge of AI safety, responsible AI practices, and PII/data privacy considerations when working with LLMs.
- Exposure to cloud platforms such as AWS, Azure, or GCP.
- Knowledge of caching frameworks (Redis, Hazelcast) for LLM response caching and rate-limit management.
- Familiarity with containerization and orchestration platforms.
- Understanding of DevOps principles and observability tools (Grafana, Prometheus, ELK) — including LLM observability (token usage, latency, hallucination monitoring).
- Opportunity to work on scalable, enterprise-level backend systems enhanced with cutting-edge AI capabilities.
- A collaborative environment with strong engineering, AI research, and domain experts.
- Continuous learning and growth opportunities through challenging technical projects at the intersection of Java and AI.
- Dedicated AI innovation time and access to LLM API credits for exploration and prototyping.
Nice to have
- Experience working in the Insurance domain (Policy, Claims, Underwriting, Billing, etc.).
- Hands-on experience building or fine-tuning ML models using Python-based frameworks (Hugging Face, scikit-learn, PyTorch) integrated with Java services.
- Exposure to AI agent orchestration tools — LangGraph, AutoGen, CrewAI, or OpenAI Assistants API.
- Experience deploying models on cloud AI services: AWS Bedrock, Azure OpenAI Service, or Google Vertex AI.
- Familiarity with MLOps platforms (MLflow, SageMaker, Kubeflow) for experiment tracking and model lifecycle management.
- Knowledge of AI safety, responsible AI practices, and PII/data privacy considerations when working with LLMs.
- Exposure to cloud platforms such as AWS, Azure, or GCP.
- Knowledge of caching frameworks (Redis, Hazelcast) for LLM response caching and rate-limit management.
- Familiarity with containerization and orchestration platforms.
- Understanding of DevOps principles and observability tools (Grafana, Prometheus, ELK) — including LLM observability (token usage, latency, hallucination monitoring).
Benefits
- Opportunity to work on scalable, enterprise-level backend systems enhanced with cutting-edge AI capabilities.
- A collaborative environment with strong engineering, AI research, and domain experts.
- Continuous learning and growth opportunities through challenging technical projects at the intersection of Java and AI.
- Dedicated AI innovation time and access to LLM API credits for exploration and prototyping.