Middle/Senior Back-end Developer + LLM

We’re looking for a Middle/Senior Back-end Developer to help build and scale an AI-powered customer support platform.

Location: Remote in Europe with ability to travel to Valencia for occasional business trips is required, typically once or twice a year for events such as hackathons.

You’ll ramp the way everyone on the team does: your first weeks focus on stability, robustness, and bug-fix work across the live chat deployment — the fastest way to learn the architecture end-to-end while contributing immediate value to production customers. As you come up to speed, you’ll take on feature and framework development with growing scope and ownership.

You don’t need to be an AI expert. You need to be a strong, fast-learning engineer who has genuinely built things with LLMs and wants to go deep on production agentic systems.

What You’ll Do

• Build features across the platform: backend services, conversational flows, and components of the agentic harness (tool/function calling, context management, guardrails)
• Extend the tool and adapter layer that exposes cloud APIs and real-time telemetry to LLMs
• Investigate and fix issues spanning LLM behavior (prompts, tool calls, context) and conventional backend causes (APIs, data, concurrency) — and build the instincts for systems where a “bug” may live in a prompt rather than in code
• Strengthen the platform’s production quality: test coverage, evaluation cases, failure handling, and observability
• Contribute to the platform’s expansion from chat into real-time voice as it develops
• Work closely with the team to understand the architecture, gradually take ownership of larger areas, and contribute to technical decisions

What You’ll Bring

• 3+ years of professional software engineering experience building backend systems
• Solid Java experience
• Hands-on practical experience with LangChain, LangGraph, RAG, and MCP — you have used these technologies in real projects and can speak concretely about what you built, how you integrated them, and what challenges you encountered
• Genuine hands-on experience building with LLMs: you’ve used LLM APIs with tool/function calling, iterated on prompts and context, and can speak concretely about what you built, what broke, and how you fixed it — from production work, internships, or substantial personal projects
• Good fundamentals: data structures, APIs, testing, debugging, and version control; familiarity with cloud services and containers
• Fast, self-directed learning — evidence you can absorb a complex architecture in weeks and then work with increasing autonomy
• Care and rigor appropriate to a production system for Tier-1 enterprise customers handling sensitive subscriber data
• Upper-Intermediate English level

Nice to Have

• Experience writing evaluations or tests for LLM-driven features
• Event-driven systems (e.g. Kafka), Kubernetes, or CI/CD experience
• Telecom, IoT, networking, or other telemetry-rich domains
• Experience with real-time systems or voice/real-time AI
• Interest in production agentic systems and LLM infrastructure