Java / AI Developer

J

Job Description:
Java Developer – AI Integration (Mid -Level)

Role Overview

We are looking for a
Mid -Level Java Developer (4 -9 years) with hands -on experience in AI integration
to join our product engineering team. In this role, you will be responsible for
embedding AI capabilities - including converting the user -provided natural
language inputs, or database metadata information into valid product
configurations, Retrieval -Augmented Generation (RAG), LLM -powered features, and
agentic workflows - directly into our Java/Spring Boot -based enterprise
product. You will work at the intersection of traditional backend engineering
and modern AI/LLM technologies, owning the design, development, and maintenance
of AI -powered components end -to -end.

Key
Responsibilities:

AI Feature
Development

• Capture user intent via a conversational/chat
  interface and translate it into valid product configurations.
  
• Extract metadata from heterogeneous sources -
  file formats (CSV, Excel, PDF), relational database schemas, and API
  contracts (OpenAPI, WSDL) and convert them to valid product
  configurations.
  
• Design and implement RAG pipelines that retrieve
  contextually relevant information from internal knowledge bases and
  surface it through LLM -generated responses.
  
• Integrate LLM -powered features such as
  intelligent chat, document summarization, content generation, and semantic
  search into the existing product.
  
• Build and maintain AI agent workflows - including
  tool use, multi -step reasoning chains, and orchestrated LLM calls - to
  automate complex product -level tasks.
  

• Author, version, and iterate on system prompts
  and user prompt templates to reliably steer LLM behavior.
  
• Apply advanced prompting techniques:
  chain -of -thought, few -shot examples, output format enforcement, and
  instruction following.
  

• Integrate with multiple LLM providers -
  OpenAI/Azure OpenAI, Anthropic Claude, AWS Bedrock, and open -source models
  (LLaMA, Mistral, etc.) - through their APIs.
  
• Build provider -agnostic abstraction layers using
  Spring AI and LangChain4j to allow model switching without core code
  changes.
  

Backend
Engineering

• Write clean, maintainable, production -grade Java
  code following SOLID principles and enterprise design patterns.
  
• Integrate AI services with existing relational
  databases via JPA/Hibernate, including schema design for storing
  conversation history, embedding metadata, and audit trails.
  
• Manage build pipelines and dependency governance
  using Maven or Gradle.
  

Testing &
Quality

• Write comprehensive unit, integration, and
  contract tests for AI -integrated components.
  
• Handle non -deterministic LLM output gracefully -
  implement fallback logic, retry strategies, and output validation.
  
• Monitor AI feature performance in production;
  instrument logging and tracing for LLM calls, including token usage,
  latency, and error rates.
  

Mandatory Skills
& Experience

Java & Backend

• 4–9 years of professional Java development
  experience.
  
• Strong proficiency in Spring Boot - REST
  controllers, service layer design, dependency injection, and configuration
  management.
  
• Experience building and consuming RESTful
  Microservices.
  
• Working knowledge of JPA/Hibernate -
  entity modelling, JPQL, transaction management.
  

AI / LLM
Engineering

• Hands -on experience integrating with at least one
  major LLM provider API (OpenAI, Azure OpenAI, Anthropic, or AWS Bedrock)
  from Java.
  
• Practical experience with Prompt Engineering - not just calling APIs, but crafting, testing, and iterating prompts to
  achieve reliable, structured outputs.
  
• Experience building RAG pipelines -
  document ingestion, chunking strategies, embedding generation, similarity
  retrieval, and context injection into prompts.
  
• Working knowledge of LangChain4j and/or Spring
  AI frameworks for LLM orchestration within Java applications.
  
• Understanding of AI agent concepts -
  tool/function calling, agent loops, multi -step task orchestration.
  

 Good -to -Have
Skills

• Experience with vector databases such as
  Pinecone, Weaviate, Qdrant, pgvector, or Chroma for embedding storage and
  semantic search.
  
• Exposure to multiple LLM providers (OpenAI, Anthropic Claude, AWS Bedrock, LLaMA/Mistral via Ollama or
  similar).
  
• Familiarity with open -source LLM deployment - running models locally or on self -hosted infrastructure.
  
• Knowledge of token budgeting, context
  window management, and cost optimization strategies for LLM APIs.
  
• Experience with observability tooling for
  AI systems - LangSmith, Helicone, or custom logging pipelines.
  
• Familiarity with streaming responses from
  LLMs (Server -Sent Events / WebSocket delivery to frontend).
  
• Understanding of embedding models - how
  they differ from generative models, when to use which, and how to evaluate
  embedding quality.
  
• Exposure to CI/CD pipelines and
  containerization (Docker, Kubernetes).