About ValeriaValeria 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 is still stuck in spreadsheets and legacy software.Your RoleYou’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.ResponsibilitiesPrototype the complex: build proofs of concept that solve hard problems in innovative ways, then take them to productionTranslate business into AI: understand the business problem deeply and land it into a solid technical solutionDesign agents end‑to‑end: multi‑agent architectures, tools, tool‑calling, function calling, memory, orchestration, and state managementMaster context engineering: decide what goes into the context window and how (system prompts, few‑shot, retrieval, memory, compaction, token management), understanding why behaviour 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 agentEnsure reliability: solid evals and observability (datasets, metrics, regressions, production tracing) before every release—never on a single happy‑pathBuild high‑quality RAG systems: embeddings, vector stores, chunking, retrieval, re‑ranking, and grounding in a compliance‑heavy contextPick the right model: integrate and compare GPT, Gemini, and Claude, reasoning about cost, latency,



reliability, context window, and fallbackIntegrate systems: build MCP servers and integrations with external systemsShip production code: solid Python, APIs, tests, CI/CD, and the team’s best practicesStay on the frontier: keep up with the latest models and technologies and test them to spot opportunitiesOwn features end‑to‑end: from technical design to deployment, monitoring, and iteration based on customer feedbackMentor interns and evangelise AI across other departments as we scale the teamRequired3 years of professional software engineering experience building production systemsStrong 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 equivalentsContext engineering and evaluation mindset: you reason about why models behave the way they do, and you validate with evals instead of a single testAbility to design and break down medium‑complexity solutions autonomously, communicating progress, blockers, and trade‑offs clearlyStartup mindset: comfortable with ambiguity, high autonomy, and fast iteration cyclesStrong communication skills and ability to collaborate across product, design, and business teamsFluent in English and/or SpanishHighly ValuedCloud 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 similarReact and basic frontend notions for full‑stack contributionsExperience deploying agents/models in production at scaleMCP, advanced function calling, and evaluation frameworks (LangSmith, RAGAS, or similar)Fine‑tuning / model optimization techniquesBackground in FinTech, HR‑tech, or regulated industries (compliance‑heavy products,



government integrations)What makes you a great fitYou’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 StackLanguage: Python, async APIs, SQL, GitAI frameworks: LangChain / LangGraphLLMs & agents: prompting, context engineering, agent harness/scaffolding, tool‑calling, multi‑step agents, RAG, embeddings, vector databasesModels & Cloud: Azure OpenAI, Gemini (GCP), ClaudeQuality & Observability: evals, testing, LLM tracing (Datadog)Channel: WhatsApp APIOur technical philosophyAs 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 OfferCompetitive compensation: €40,000‑45,000 gross salary EquityFree lunch when you’re at the office thanks to Kombo & NoraFlexible remuneration with CoverflexFlexibility: Hybrid setup (HQ in Barcelona), 60 days/year remote work from anywhereUnlimited vacation days—take the time you need, no counting daysOur hiring processIntro call with People (30 min)Interview with the Hiring Manager (45 min)Tech Assessment – Onsite at the office (1 hour)Founders interview (45 min)OfferWhy 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 AIMarket opportunity: €5B market in Spain, every company with employees needs payroll, and current solutions are outdated and painfulReal AI ownership: you won’t assist on AI projects—you’ll build them, decide on them, and see their impact on thousands of peopleCareer growth: be a critical AI hire, build the playbook, and grow as we scale the team
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📌 Mid AI Engineer (Barcelona)
🏢 Tamarind Intelligence
📍 Barcelona