About Galtea
Galtea is building the evaluation platform for AI teams. We help companies ship better AI, faster, by making it easy to test, evaluate, and monitor AI agents and LLM-powered products at scale. We're backed by top-tier investors and working with some of the most sophisticated AI teams in Europe, including major financial institutions and tech companies.
We're a small, high-conviction team. Every hire matters.
The role
As a Forward Deployed Engineer, you'll sit at the intersection of engineering and customer success, owning the technical relationship with our enterprise accounts from day one.
You'll be the person who makes sure customers succeed. You'll onboard them, integrate their systems, understand their AI stack, and work directly with their engineering teams to get real value out of Galtea.
You'll also build. When an account needs a metric that doesn't exist yet, a judge that doesn't hold up, or a simulator that doesn't behave like a real user, you open a pull request against our AI services and fix it. Reporting the gap is where the job starts.
This is a role for someone who loves working with customers and loves getting their hands dirty technically.
What you'll do
— Own enterprise onboarding. Set up customers on the platform, guide them through their first evaluation runs, and help them integrate Galtea's SDK into their existing pipelines.
— Be the technical point of contact for our enterprise accounts. Join customer calls, answer technical questions, debug issues, and build trust with their engineering teams.
— Understand customer AI stacks. Learn how each customer is deploying their agents and LLMs, and advise on how to structure evaluations for their specific use cases.
— Develop and improve our AI services. Custom metrics, LLM judges, conversation simulators, red-teaming scenarios. You build them for the account in front of you, then harden the ones that generalise so the next customer gets them for free.
— Work AI-first on your own job. Coding agents, evaluation harnesses, generated test data, automated triage. One engineer covering four enterprise accounts only works if agents are doing the repetitive half, and we expect you to bring back the workflows that paid off so the rest of the team picks them up.
— Bridge customers and product. Systematically capture customer feedback, reproduce issues, and communicate insights to the product team so we build the right things. Ship the fix yourself when it sits in your half of the stack.
— Write internal documentation and playbooks. Help the team scale what you learn by creating repeatable processes for onboarding and support.
— Collaborate with sales. Bring technical credibility into the room where deals get scoped and won.
What we're looking for
Must have
— 2+ years of software/AI engineering or solutions engineering experience.
— Strong Python skills. You can write scripts, read SDK code, and debug integrations comfortably. Your code is good enough to merge into a product other engineers maintain.
— Deep understanding of the AI evaluation problem. You know why evaluating LLMs and AI agents is hard, and you have a point of view on how to do it well.
— You already work with coding agents daily. You have opinions about where they earn their keep and where they waste your time.
— Excellent communication. You can explain technical concepts clearly to both engineers and non-technical stakeholders.
— Customer-first mindset. You take ownership of customer problems and don't drop balls.
Nice to have
— Experience at an enterprise SaaS company or AI/ML tooling company.
— Familiarity with MLOps, LLMOps, or AI evaluation concepts.
— You've built LLM-powered components that run in production: judges, agents, RAG pipelines, not just consumed them.
— Experience working with large financial institutions or heavily regulated industries.
— Spanish.
What we offer
— A foundational role in a fast-growing AI infrastructure company.
— Direct exposure to the most sophisticated AI engineering teams in Europe.
— Competitive salary + equity (ESOP).
— Hybrid setup in Barcelona.
— Paid AI tooling and model credits, and the expectation that you use them.
— A small, high-ownership team where your work has real impact from day one.