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
