03 ott - Milano
Pillar
p(Based in Milan or Barcelona Office) /p /brpPillar was founded in ****.
Today, we are 80+ people, serve 900+ customers, raised €12 million in ****, and are expanding internationally.
/p /brpWe are building the financial operating system for construction: bringing AI, automation, and modern software to an enormous industry still full of slow, fragmented processes.
/p /brpbWe are looking for an AI Engineer who knows how to turn LLMs into reliable product experiences, moves fast, and wants to build.
/b /p /brh3The mission /h3 /brpBuild production-grade AI systems that solve real customer problems.
/p /brpWe want someone who can move beyond prototypes and build agents, retrieval systems, document pipelines, and AI-native product features that are reliable, measurable, and deeply integrated into Pillar.
/p /brh3What you will do /h3 /brul /brlipOwn AI features end to end, from product problem to production.
/p /li /brlipDesign and build agentic workflows using tool orchestration, structured outputs, and multi-step reasoning.
/p /li /brlipBuild RAG and semantic search pipelines using embeddings, vector databases, hybrid search, and re-ranking.
/p /li /brlipDevelop document extraction workflows that turn invoices, reports, and forms into structured data.
/p /li /brlipBuild observability and evaluation systems for AI features, including tracing, experimentation, and automated quality checks.
/p /li /brlipOptimize context, retrieval, and prompting strategies to improve accuracy and product quality.
/p /li /brlipWork across our TypeScript stack when AI features require it, from backend logic to product integration.
/p /li /brlipUse AI as a native part of your own engineering workflow, not only as something you build for customers.
/p /li /brlipMove quickly from ambiguity to working product, making pragmatic technical decisions along the way.
/p /li /br /ul /brh3Our stack /h3 /brul /brlipbProduct: /b Next.js · React · TypeScript /p /li /brlipbBackend data: /b Supabase · Node/Deno · Postgres /p /li /brlipbAI: /b Mastra · LLM APIs · embeddings · vector search · AI-native workflows /p /li /br /ul /brh3Who we are looking for /h3 /brpbAI engineers with product mindset and high agency /b /p /brul /brlipYou have experience building LLM-based applications in production, not only prototypes.
/p /li /brlipYou are comfortable with agents, tool use, structured outputs, and orchestration.
/p /li /brlipYou have hands?on experience with RAG, embeddings, vector databases, and retrieval strategies.
/p /li /brlipYou are comfortable with TypeScript or can become productive in it quickly.
/p /li /brlipYou have worked with AI orchestration frameworks such as Mastra, LangChain, CrewAI, or similar.
/p /li /brlipYou are strong with SQL and relational databases.
/p /li /brlipYou understand prompt design, evaluation, and the trade-offs involved in building reliable AI systems.
/p /li /brlipYou have worked in a startup or scaleup environment and understand the pace, ambiguity, and ownership it requires.
/p /li /brlipYou take initiative, get your hands dirty, and get things done without waiting for perfect instructions.
/p /li /brlipYou can zoom out to understand the full product problem and zoom in when technical details matter.
/p /li /brlipYou communicate openly, learn quickly, and collaborate without ego.
/p /li /br /ul /brh3How we work /h3 /brul /brlipAgile and squad-based: small teams own their domains, with no unnecessary layers.
/p /li /brlipAI-first: we build and work in the age of AI.
/p /li /brlipProduct ownership: engineers are hands?on and accountable for delivered value.
/p /li /brlipBottom?up: initiative is expected, not delegated.
/p /li /br /ul /brh3The profile that can succeed /h3 /brpbAI-native · Product-minded · Startup DNA /b /p /brpThe sweet spot: a hands?on engineer who has already built real AI products, ideally in a startup or scaleup environment, and knows how to combine LLM capabilities, software engineering, data, and product judgment to ship reliable features.
/p /brh3Location languages /h3 /brpMilan or Barcelona; professional working English.
/p /brh3Hiring process /h3 /brpb01: /b CV screening.
/p /brpb02: /b 30-min Talent Call - motivation, Pillar knowledge, mindset, and startup fit.
/p /brpb03: /b Two 30-min Technical Interviews - one focused on AI systems and architecture, one focused on product engineering and execution.
/p /brpb04: /b Final Interview with our CTO.
/p /brh3What we offer /h3 /brul /brlipGross salary b55,******,000 € /b depending on experience.
/p /li /brlipThe opportunity to bbuild core parts of Pillar's AI product and engineering foundations /b as we scale.
/p /li /brlipbEnd-to-end ownership /b: from AI architecture and experimentation to shipping features used by real customers.
/p /li /brlipThe chance to work in an bAI-native engineering team /b, where AI is part of both the product and the way we build it.
/p /li /brlipClose collaboration with bProduct, AI, and Engineering /b, with direct exposure to the problems we are solving for customers.
/p /li /brlipbReal autonomy, responsibility, and room to grow /b as Pillar and the engineering team scale.
/p /li /br /ul #J-*****-Ljbffr
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