3+ years of strong software engineering experience with Java, Spring Boot, Microservices, and REST APIs.
Hands-on experience developing production-grade GenAI and LLM applications, including prompt engineering and response evaluation.
Proficient in RAG architectures, focusing on embeddings, document ingestion, and semantic retrieval.
Strong knowledge of Python and FastAPI for building AI/ML services integrated with Java applications.
Experience with Vector Databases and GenAI frameworks like Pinecone, Weaviate, and Hugging Face.
Familiarity with cloud platforms (AWS, Azure, GCP) and modern deployment practices including Docker and CI/CD.
Exposure to Agentic AI, LLMOps, and event-driven microservices is a plus.
Responsibilities
Design, develop, and maintain scalable backend applications and REST APIs using Java and Spring Boot.
Build and integrate production-grade GenAI and LLM applications with existing enterprise systems.
Implement Retrieval-Augmented Generation (RAG) pipelines for document processing and semantic search.
Develop AI services using Python and FastAPI, leveraging frameworks for enterprise AI capabilities.
Optimize Vector Databases for knowledge retrieval and semantic search functionalities.
Integrate LLM platforms, applying prompt engineering and optimization techniques for accuracy.
Ensure security and observability of AI/backend services through best practices in authentication and monitoring.
Containerize and deploy applications using Docker and CI/CD, collaborating with cross-functional teams.
Benefits
Opportunity to work with cutting-edge Generative AI technologies.
Collaborative environment with cross-functional teams including architects and data engineers.
Focus on developing scalable and secure enterprise applications.
Exposure to a variety of modern frameworks and cloud platforms.
Potential for professional growth in the rapidly evolving AI field.
Full Job Description
GenAI Fullstack Engineer | About the Role
As a GenAI Fullstack Engineer, you will design and build scalable enterprise applications that combine robust Java-based backend engineering with modern Generative AI capabilities. You will work across Java, Spring Boot, Microservices, Python, FastAPI, LLMs, RAG, and Vector Databases to develop production-ready AI services and integrate them with enterprise applications. The ideal candidate is a strong software engineer who has expanded into hands-on GenAI engineering and can develop secure, scalable solutions spanning traditional backend services, AI orchestration, enterprise knowledge retrieval, and LLM-powered applications.
GenAI Fullstack Engineer | Day-to-Day
Design, develop, and maintain scalable backend applications and REST APIs using Java, Spring Boot, Microservices, Spring Security, and Spring Data/JPA.
Build and integrate production-grade GenAI and LLM applications, connecting AI services with existing Java/Spring Boot enterprise applications and business systems.
Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking, embeddings, indexing, semantic search, context retrieval, reranking, and response generation.
Develop AI services and orchestration components using Python and FastAPI, leveraging frameworks such as LangChain, LangGraph, LlamaIndex, or Hugging Face to support reusable enterprise AI capabilities.
Implement and optimize Vector Databases such as Pinecone, Weaviate, OpenSearch, FAISS, or ChromaDB to enable enterprise knowledge retrieval and semantic search.
Integrate LLM platforms such as Azure OpenAI, OpenAI, AWS Bedrock, Claude, Gemini, or Llama, applying prompt engineering, response evaluation, guardrails, and optimization techniques to improve solution accuracy and reliability.
Build secure and observable AI/backend services by implementing authentication, authorization, exception handling, logging, monitoring, API security, and production-grade engineering standards.
Containerize and deploy applications using Docker and CI/CD pipelines, collaborating with architects, data engineers, developers, and business teams to deliver reliable and scalable GenAI solutions.
GenAI Fullstack Engineer | Skills & Experience
Strong software engineering 3+ years experience with Java, Spring Boot, Microservices, and REST APIs, including hands-on experience designing scalable and distributed enterprise backend applications.
Hands-on experience developing production GenAI/LLM applications, including LLM integration, prompt engineering, response evaluation, and enterprise AI use cases.
Strong production experience with RAG architectures, including embeddings, document ingestion, chunking, semantic retrieval, reranking, context construction, and RAG quality optimization.
Strong working knowledge of Python and FastAPI, with experience building AI/ML services and integrating Python-based AI components with Java/Spring Boot applications.
Hands-on experience with Vector Databases and GenAI frameworks, such as Pinecone, Weaviate, OpenSearch, FAISS, ChromaDB, LangChain, LangGraph, LlamaIndex, or Hugging Face.
Experience with cloud and modern deployment practices, including AWS, Azure, or GCP, Docker, CI/CD, monitoring, observability, and production deployment of AI services.
Additional exposure to Agentic AI, MCP (Model Context Protocol), AI guardrails, LLMOps, Kubernetes, knowledge graphs, multimodal AI, and event-driven microservices is advantageous.
