Generative AI + Java

We are seeking an agile and highly skilled Senior Java Backend & Generative AI Engineer to join our rapidly evolving AI Platform team. This position is tailored for a strong backend engineer who has successfully transitioned into or heavily adapted their core skill set to build production-grade Generative AI pipelines and application logic.

You will own the development of secure highly scalable microservices while orchestrating enterprise LLM connections. You will bridge the gap between robust enterprise-grade Java backends and modern intelligent AI workflows.

• AI Backend Engineering: Design develop and implement scalable fault-tolerant backend microservices and RESTful APIs using Java (11/17) and the Spring Boot framework.

• GenAI Application Delivery: Lead the integration of Large Language Models (LLMs) into existing commercial software ecosystems executing structural prompt engineering and tool-calling flows.

• RAG & Search Layer Optimization: Build and maintain high-efficiency Retrieval-Augmented Generation (RAG) systems configuring semantic search indexing embedding pipelines and vector database retrieval.

• Cloud & Model Orchestration: Leverage AWS Bedrock and related cloud-native environments to consume route and optimize foundation models efficiently.

• Cross-Language Scripts: Write and debug orchestration code or data-processing scripts natively in Python to support AI data loaders data chunking frameworks and model evaluation metrics.

• DevOps & Code Quality: Containerize software components using Docker maintain structured Git branching strategies and support automated CI/CD workflows for reliable deployment.

Mandatory Core Skills

• Overall Experience: 4.5 to 6 years of dedicated commercial software engineering experience.

• Primary Backend Stack: Expert-level execution in Java Backend development including deep hands-on mastery over Spring Boot Spring Boot Data/JPA and microservices design patterns.

• Generative AI Foundation: Proven technical experience building applications that interact with commercial or open-source LLM APIs (OpenAI Anthropic or Llama).

Good to Have (High Priority)

• Direct experience orchestrating foundational models natively via AWS Bedrock.

• Practical implementation of RAG architectures including text-chunking techniques and vector database workflows (e.g. Pinecone FAISS PgVector or OpenSearch).

• Mid-level coding proficiency in Python for configuring open-source AI frameworks (LangChain LlamaIndex or data processing stacks like Pandas/NumPy).