AI-native B2B SaaS Scale-up We aren't looking for someone to add an LLM API to an existing product. Our client is a fast-growing European B2B software company helping distributed organisations automate high-volume operational and compliance-heavy workflows. Its platform combines enterprise data, documents, external systems and AI agents to automate work that historically required people to move information between emails, PDFs, legacy applications and back-office systems.

The company has passed the experimental stage. Customers already use AI-powered workflows in production. What belongs in the prompt, retrieval layer or deterministic software?

As Lead

Data & AI Engineer , you'll be the senior technical reference for a small group of Data and AI Engineers. Expect approximately 60-70% hands-on engineering , with the rest spent on architecture, technical direction, mentoring and working with Product, Security and Data. Decide where deterministic software should replace probabilistic model behaviour.

Design AI-ready data architectures and interfaces between enterprise data and AI services. Own LLMOps/MLOps patterns for versioning, testing, rollout and rollback. Partner with Security and Legal on access control, privacy, responsible AI and EU regulatory requirements.

Core: Python, FastAPI, SQL, Git AI: Azure OpenAI, Azure AI Foundry, LangGraph / Semantic Kernel Retrieval: embeddings, vector/hybrid search, re-ranking Data & ML: Databricks, MLflow Platform: containers, Kubernetes, Terraform, CI/CD Observability: distributed tracing, AI eval tooling,



production monitoring We care more about whether you understand the underlying engineering problem than whether you've used one particular framework. 7+ years in software, data or ML engineering , with meaningful production ownership. ~ Strong Python/software-engineering fundamentals. ~ Hands‐on experience taking an LLM, RAG or agent‐based system beyond prototype stage and into production. ~ Experience with cloud architecture, APIs and distributed systems. ~ A strong evaluation mindset: "it seemed to work in testing" isn't enough. ~ Ability to reason across data, ML and software architecture rather than treating them as separate worlds. ~ Strong professional English. Enterprise search, MCP, model fine-tuning, responsible AI, Azure AI, MLflow, Databricks, event-driven architectures or experience building software in regulated domains. After your first months, the team should be able to answer questions it currently can't answer reliably: Which retrieval change improved quality?

And, crucially: should this particular problem use an LLM at all? Annual performance bonus and/or equity package. ~ Permanent contract. ~ Hybrid setup: around two office days per week in Barcelona or Madrid. ~ Private health insurance. ~ Training and conference budget. ~25+ working days' holiday. ~ High-end equipment and home-office support. ~ AI systems deep dive: 75 min You'll discuss a system you've built and design a production AI architecture with one of our senior engineers. No five-round interview marathon and no take-home project that consumes your weekend.

Depending on current team needs, the process may lead to an immediate opportunity or to our priority talent network.

📌 Lead/Senior Data Engineer (Cataluña)
🏢 Value Crew
📍 Cataluña