About Valeria
Valeria is building the future of HR and payroll in Spain. We're an AI-native platform that automates contracts, payroll, and compliance for companies with high employee turnover (hospitality, delivery, events, agriculture). We're rethinking how an entire industry works—moving from manual, error-prone processes to intelligent automation.
We're a fast-growing startup backed by top investors, disrupting a €5B+ industry that's still stuck in spreadsheets and legacy software.
Your Role
You'll work closely with the AI Lead, designing product AI features and transforming internal processes with AI—helping build a cross-functional AI team with impact across every department. You'll be a core member of our AI team, building the agents that power Valeria in production—talking to real customers and handling real payroll and legal processes. You'll own AI features end-to-end: designing agent architectures, engineering context, building evals that hold up, and shipping reliable systems into a domain where correctness genuinely matters.
Prototype the complex: build proofs of concept that solve hard problems in innovative ways, then take them to production
Translate business into AI: understand the business problem deeply and land it into a solid technical solution
Design agents end-to-end: multi-agent architectures, tools, tool-calling, function calling, memory, orchestration, and state management
Master context engineering: decide what goes into the context window and how (system prompts, few-shot, retrieval, memory, compaction, token management), understanding why behavior changes and anticipating failure modes (hallucinations, edge cases, prompt injection)
Build the LLM harness: the layer around the model—tool interfaces, output parsing and validation, retries, fallbacks, guardrails, scaffolding, and flow control—that turns a model into a reliable production agent
Ensure reliability: solid evals and observability (datasets, metrics, regressions, production tracing) before every release—never on a single happy-path
Build high-quality RAG systems: embeddings, vector stores, chunking, retrieval, re-ranking, and grounding in a compliance-heavy context
Pick the right model: integrate and compare GPT, Gemini, and Claude, reasoning about cost, latency, reliability, context window, and fallback
Integrate systems: build MCP servers and integrations with external systems
Ship production code: solid Python, APIs, tests, CI/CD, and the team's best practices
Stay on the frontier: keep up with the latest models and technologies and test them to spot opportunities
Own features end-to-end: from technical design to deployment, monitoring, and iteration based on customer feedback
Mentor interns and evangelize AI across other departments as we scale the team
Required:
3+ years of professional software engineering experience building production systems
Strong Python skills and solid backend fundamentals (APIs, SQL, Git, testing)
Hands-on experience with LLMs / agents in production: LangChain/LangGraph, RAG, prompting, tool-calling, or equivalents
Context engineering and evaluation mindset: you reason about why models behave the way they do, and you validate with evals instead of a single test
Ability to design and break down medium-complexity solutions autonomously, communicating progress, blockers, and trade-offs clearly
Startup mindset: comfortable with ambiguity, high autonomy, and fast iteration cycles
Strong communication skills and ability to collaborate across product, design, and business teams
Fluent in English and/or Spanish
Highly Valued:
Cloud experience: Azure (Azure OpenAI / AI Foundry) and GCP (Vertex AI / Gemini)
Hands-on practice / familiarity with AI coding tools such as Claude Code, Cursor, Codex, and similar
React and basic frontend notions for full-stack contributions
Experience deploying agents/models in production at scale
MCP, advanced function calling, and evaluation frameworks (LangSmith, RAGAS, or similar)
Fine-tuning / model optimization techniques
Background in FinTech, HR-tech, or regulated industries (compliance-heavy products, government integrations)
️ What makes you a great fit: You're the kind of engineer who treats LLMs as systems to be understood, not black boxes to be prompted once. You care about reliability, you anticipate how agents fail, and you build the harness and evals that keep them honest in production. You're pragmatic but principled, comfortable moving fast in a startup, and excited to work in a domain where correctness matters—getting payroll wrong affects real people's lives. Bonus points if you love being on the frontier of applied AI.
Our Stack
Language: Python, async APIs, SQL, Git
AI frameworks: LangChain / LangGraph
LLMs & agents: prompting, context engineering, agent harness/scaffolding, tool-calling, multi-step agents, RAG, embeddings, vector databases
Models & Cloud: Azure OpenAI, Gemini (GCP), Claude
Quality & Observability: evals, testing, LLM tracing (Datadog)
Channel: WhatsApp API
Our technical philosophy:
As an AI-native product, we build intelligence into every layer—automating altas, bajas, payroll, and compliance through agents that run in production, not demos. We care about reliable, testable systems over framework magic, and we treat evals and observability as first-class. If you're excited about applying AI to solve real business problems (not building AI for AI's sake), you'll love working here.
️ What We Offer
Competitive compensation: €40.000 - €45.000 gross salary + Equity
Free lunch when you're at the office thanks to Kombo & Nora
Flexible remuneration with Coverflex
Flexibility: Hybrid setup (HQ in Barcelona), 60 days/year remote work from anywhere
Unlimited vacation days — take the time you need, no counting days
️ Our hiring process
Intro call with People (30 min)
Interview with the Hiring Manager (45 min)
Tech Assessment - Onsite at the office (1 hour)
Founders interview (45 min)
Offer
Why Join Valeria Now?
Timing: We're past the "idea stage" with real customers and revenue, but early enough that you'll define how we scale our AI
Market opportunity: €5B+ market in Spain, every company with employees needs payroll, and current solutions are outdated and painful
Real AI ownership: you won't assist on AI projects—you'll build them, decide on them, and see their impact on thousands of people
Career growth: be a critical AI hire, build the playbook, and grow as we scale the team
