GenAI Fullstack Engineer

Marlabs • Englewood Cliffs, NJ

5d • Onsite

About The Position

Marlabs is hiring a GenAI Fullstack Engineer to design and build scalable enterprise applications combining Java-based backend engineering with Generative AI capabilities. The role spans Java, Spring Boot, microservices, Python, FastAPI, LLMs, RAG, and vector databases, developing production-ready AI services and integrating them with enterprise applications. The engineer will build secure, scalable solutions across backend services, AI orchestration, enterprise knowledge retrieval, and LLM-powered applications. This position requires working onsite five days per week.

Requirements

• At least 3 years of software engineering experience with Java, Spring Boot, microservices, and REST APIs, including 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.
• 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, including 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 AWS, Azure, or GCP; Docker; CI/CD; monitoring; observability; and production deployment of AI services.

Nice To Haves

• Exposure to Agentic AI, MCP (Model Context Protocol), AI guardrails, LLMOps, Kubernetes, knowledge graphs, multimodal AI, or event-driven microservices.

Responsibilities

• 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 with existing Java/Spring Boot enterprise applications and business systems.
• Design and implement 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, with frameworks such as LangChain, LangGraph, LlamaIndex, or Hugging Face.
• Implement and optimize vector databases to support enterprise knowledge retrieval and semantic search.
• Integrate LLM platforms and apply prompt engineering, response evaluation, guardrails, and optimization techniques to improve accuracy and reliability.
• Implement authentication, authorization, exception handling, logging, monitoring, API security, and other production-grade engineering practices for secure and observable services.
• Containerize and deploy applications using Docker and CI/CD pipelines.
• Collaborate with architects, data engineers, developers, and business teams to deliver reliable and scalable GenAI solutions.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed