Backend AI Developer

We are looking for a Backend AI Developer:

This role involves building an AI-powered tool designed for enterprise clients. It sits at the intersection of strong backend Java engineering and applied AI, focusing on designing and developing intelligent agents using Java and Spring-based technologies.

Responsibilities:

• Design, build, and deploy AI agents and agentic workflows using Java and Spring Boot.
• Develop production-quality APIs and backend services using Java 21 and Spring Boot 3.x.
• Implement agent capabilities including tool/function calling, memory and context management, planning, orchestration, retries, guardrails, validation, and multi-step workflows.
• Use Spring AI or LangChain4j to integrate LLMs and orchestrate agent workflows within Spring applications.
• Design effective LLM prompts using templates, roles, constraints, and structured outputs.
• Build and maintain LLM evaluations to measure agent quality, reliability, and performance.
• Implement LLM observability and monitoring, including tracing, latency, token usage, cost, and failure analysis.
• Apply RAG techniques, including chunking, embeddings, vector databases, and retrieval optimization.
• Use AI coding tools such as GitHub Copilot or Amazon Q as part of the daily software development workflow.
• Collaborate with engineers and stakeholders to prototype, test, and continuously improve AI capabilities.
• Write clean, maintainable, well-tested Java code while incorporating AI capabilities into production applications.

Requirements:

• Strong professional experience with Java, including modern Java versions such as Java 21.
• Strong experience with Spring Boot 3.x, including Spring Web, Spring Data, and Spring Security.
• Proven ability to build and support production APIs and backend services.
• Strong understanding of software engineering fundamentals, testing, debugging, and API design.

Required Skills:

• Hands-on experience building AI agents or agentic applications.
• Experience with Spring AI, LangChain4j, or a comparable agent/LLM orchestration framework.
• Understanding of agent architecture, including tool calling, memory, planning, orchestration, retries, and guardrails.
• Hands-on experience with LLM prompt design and prompt engineering.
• Experience designing or developing LLM evaluations.
• Understanding of RAG fundamentals, including embeddings, chunking, vector databases, and retrieval tuning.
• Experience with at least one LLM observability/monitoring platform, such as Langfuse, Arize, Weights & Biases, or Datadog.
• Daily hands-on experience using AI coding assistants, such as GitHub Copilot or Amazon Q.
• Comfortable incorporating AI into development, testing, debugging, refactoring, and code-generation workflows.

Preferred Skills:

• Experience optimizing LLM applications for performance and cost, including caching, batching, model routing, and prompt/token optimization.
• Experience evaluating and selecting AI models based on quality, latency, cost, and risk.
• Experience deploying AI services to AWS, Azure, or GCP.
• Experience with CLI-based coding assistants such as GitHub Copilot CLI or Claude Code.
• Experience with spec-to-code development, AI-generated testing, or automated refactoring.
• Experience with MCP (Model Context Protocol) or OpenAPI-based tool schemas.
• Experience integrating AI agents with enterprise APIs, databases, and external tools.