Backend AI Developer

We are looking for a Backend AI Developer

This role involves building AI-powered solutions for enterprise clients, focusing on strong backend Java engineering and applied AI. You will be responsible for creating reliable, production-ready AI agents and LLM-powered applications.

Responsibilities

• Design, build, and deploy AI agents and agentic workflows using Java 21, Spring Boot 3.x, Spring AI, or LangChain4j.
• Build production APIs and backend services using Spring Web, Spring Data, and Spring Security.
• Implement agent capabilities including tool calling, orchestration, memory/context, multi-step workflows, retries, and guardrails.
• Design prompts and structured outputs and apply RAG, including embeddings, chunking, vector databases, and retrieval optimization.
• Design and maintain LLM evaluations (evals) to measure quality, reliability, and performance.
• Evaluate and select AI models based on quality, latency, cost, and risk.
• Optimize LLM applications for performance and cost, including caching, batching, model routing, and prompt/token optimization.
• Implement LLM observability and monitoring using tools such as Langfuse, Arize, Weights & Biases, Datadog, or similar to track traces, latency, token usage, cost, and failures.
• Use AI coding tools such as GitHub Copilot, Amazon Q, or Claude Code throughout development, testing, debugging, and refactoring.
• Collaborate with engineers and stakeholders to prototype, test, and continuously improve AI capabilities while maintaining clean, well-tested Java code.

Requirements

• Strong professional experience with Java, including modern Java versions such as Java 21.
• Strong experience with Spring Boot 3.x and production APIs/backend services.
• Strong understanding of software engineering fundamentals, testing, debugging, and API design.

Required Skills

• Hands-on experience building and deploying AI agents or agentic applications to production.
• Experience with Spring AI, LangChain4j, or comparable LLM/agent frameworks.
• Understanding of agent architecture, including tool calling, orchestration, memory, retries, and guardrails.
• Hands-on experience with LLM prompt design and RAG.
• Experience designing or developing LLM evaluations (evals).
• Experience with LLM observability/monitoring in a production environment.
• Daily hands-on experience with AI coding assistants such as GitHub Copilot, Amazon Q, or Claude Code.
• Experience incorporating AI into coding, testing, debugging, and development workflows.
• Experience with spec-driven or spec-to-code development is highly preferred.

Preferred Skills

• Experience optimizing LLM applications for performance and cost.
• Experience benchmarking and selecting models based on quality, latency, cost, and risk.
• Experience with MCP, OpenAPI-based tool schemas, or enterprise API/tool integrations.
• Experience deploying AI services to AWS, Azure, or GCP.
• Experience with CLI-based coding assistants such as Copilot CLI or Claude Code.