AI Developer - KLPTK
About the role
- Design, build and run LLM-powered features and services – from first prototype to monitored production
- Build retrieval-augmented generation (RAG) pipelines over product documentation, configuration knowledge and support history: ingestion, chunking, embeddings, search and ranking
- Develop agentic workflows and tool integrations (e.g. via the Model Context Protocol) that let AI work safely with our APIs, ticketing systems and business data
- Integrate AI services with our Java backend services, REST APIs and Oracle databases
- Build evaluation suites and test sets so we know – with numbers – whether a change makes an AI feature better or worse
- Engineer prompts, context and guardrails; handle hallucination, data privacy, access control and cost from the start, not as an afterthought
- Monitor AI features in production: quality, latency, token cost and failure modes – and improve them continuously
- Work with support, product and operations colleagues to turn real use cases into tools people actually use
- Collaborate asynchronously and synchronously with the Italian development team and cross-functional stakeholders
- Participate in code reviews – give and receive honest, constructive feedback
- Share what you learn: help other developers adopt AI-assisted development and write documentation the next person can actually use
- Use AI coding tools actively and critically in your own work – while taking full ownership of what ships
- How You WorkWe're hiring for mindset as much as skills. The right person for this role:
- Is genuinely curious and self-driven – you follow the field because you want to, and you can separate real progress from hype
- Is sceptical by default – you measure before you believe, and you know a good demo is not proof
- Thinks in systems – you consider how models, data, APIs and people interact, where things break, and how to design for resilience, not just the happy path
- Solves problems even when the path isn't obvious – you investigate, experiment, and iterate
- Thinks about the user – you build AI features that fit how people actually work, not features that show off the technology
- Communicates clearly and proactively in a distributed team – you don't wait to be chased, you flag blockers early and keep colleagues informed
- Challenges ideas constructively and is comfortable being challenged in return
- Takes ownership of features end-to-end, not just your assigned ticket
- Is comfortable with ambiguity and fast-changing tools and requirements
- Proven experience building and shipping LLM-based applications to real users – not only experiments or tutorials
- Hands-on experience with LLM APIs (e.g. Anthropic Claude, OpenAI, Azure OpenAI) including tool use / function calling and structured outputs
- Practical RAG experience: embeddings, vector or hybrid search, chunking strategies, and judging retrieval quality
- Solid software engineering in Python and/or Java – clean, tested, maintainable code; comfortable working in an existing Java backendAPI and integration skills: REST, JSON, authentication, and connecting services to existing business systems
- Relational database fluency – strong SQL; Oracle experience preferred, PostgreSQL or MySQL accepted
- A habit of evaluating AI output systematically – test sets, metrics, regression checks – rather than by impression
- Strong problem-solving skills and the ability to work independently and collaboratively
- Experience with Agile methodologies and fast-paced delivery cycles
- Professional-level English, written and verbal – your primary working language with the Italian team
- Agent frameworks and protocols (Model Context Protocol, LangChain/LangGraph, LlamaIndex, Spring AI or similar)
- Experience with vector databases or search engines (pgvector, Oracle AI Vector Search, Elasticsearch/OpenSearch, Azure AI Search)LLM observability and evaluation tooling (e.g. Langfuse, LangSmith, promptfoo or your own)
- Java with Spring Boot or JAX-RS, JPA/Hibernate
- Containerisation and deployment (Docker, Kubernetes) and CI/CD pipelines (Jenkins, GitHub Actions or equivalent)
- Cloud experience, especially Azure (also AWS)
- Security for AI systems: prompt injection, data leakage, access control, GDPRFamiliarity with Keycloak, OpenID Connect, OAuth 2 or JWTFrontend basics (TypeScript, Angular) to build simple user interfaces for AI features
- Domain experience in publishing, rights & royalties, or enterprise support and service management
Before you apply
Applying takes about a minute. These four things decide how fast it moves after that.
Your profile is current
It's what we read first. Occupations, seniority and locations matter more than a long history.
Two examples you can talk through
Not a portfolio — just two pieces of work where you can explain the decisions and what you'd change.
A number in mind
What you're on now and what would make you move. We negotiate better when we know both.
Your notice period
Employers plan around it, and it's the question that stalls offers most often.
Once you apply, someone reads it and calls you before anything reaches the employer — usually within two working days.
