We're the AI-first CRM that turns conversations into revenue. On-site, WhatsApp, email, voice: our AI Agents sell, support, and retain customers for the best B2C brands out there, autonomously.
We believe the future of commerce is warmer, more human than ever - just at a scale no human team could pull off alone.
We've raised €3M+ and work with brands like Ducati, Pittarosso, MC2 Saint Barth, and Doppelgänger. We're a lean, fast, Italian-born scale-up now expanding our story internationally and this role is a big part of writing it.
Join our AI team to build the agentic runtime for proactive agents: context building, tool/retrieval orchestration, scoring, and guardrails. You’ll own production components from day one and work directly on the “signal vs. noise” call.
Grounding. The agent sometimes says a product is unavailable when it's in stock. Fixing that is a retrieval problem, a prompt problem, and an eval problem at the same time.
Exact match vs. semantic. Semantic search is good at "warm jacket for winter" and bad at a SKU, a size, or a specific variant. We need both, in one ranking.
Multilingual quality. Our customers write in Italian, English, and more. An agent that answers in the wrong language is worse than one that says nothing.
Latency and cost. A customer is waiting on their phone. You'll profile requests, test prompt and model changes, and ship improvements you can point at on a graph.
Evals that catch problems before customers do. Every conversation has a Langfuse trace, and we have a golden-dataset harness. Making it actually predict production quality is open work.
Deciding what's worth saying. Ranking, scoring, and suppressing proactive insights so the agent interrupts a customer only when it has earned it.
2+ years shipping software professionally, including at least one LLM feature real users touched — a chatbot, a RAG system, an agent. Not a weekend project.
You know how RAG actually behaves: embeddings, vector search, chunking, reranking, and where each one breaks.
You can read traces. Given a bad answer in production, you can find why: retrieval missed, the prompt was wrong, the model picked the wrong tool. Langfuse, LangSmith, Braintrust, Arize, Humanloop — any of them.
Strong Python and backend fundamentals, and the instinct to measure latency, cost and quality instead of guessing.
Nice to have: search infra (Elastic, Algolia, Qdrant, Weaviate), fine-tuning, graph search, you know how e-commerce operate.
Real ownership, from day one. No layers, no waiting your turn: you'll own outcomes, not just tasks.
Lean team, big impact. You'll work shoulder-to-shoulder with top-notch international talent, ready to disrupt and leave a mark.
Build, don't maintain. We're early enough that most of what you touch doesn't have a playbook yet, you get to write it.
Grow as fast as you push. Roles and responsibilities scale with what you prove you can do, not with tenure.
A team that actually likes each other. We're a dynamic, close-knit crew that moves fast, laughs a lot, and has each other's backs.
We keep it real and reasonably fast:
Screening call, let's just talk. Communication, fit, the basics.
Interview + live case study, Let’s deepdive into technicalities of the role.
Founder & Team conversations, here you meet the people you'd actually work with.
Debrief, reference check, and decision. We move quickly once we're there.
Sounds like you? If you want to own something real from day one and help write the UK growth story, we want to meet you!
TextYess is an equal opportunity employer. We welcome applicants of all backgrounds and experiences.
Skills Required
- At least 2 years of professionally shipping software
- Experience shipping at least one LLM feature used by real users, such as a chatbot, RAG system, or agent
- Practical knowledge of RAG, including embeddings, vector search, chunking, and reranking
- Ability to analyze production traces and diagnose retrieval, prompt, or tool-selection issues
- Experience with an LLM observability or evaluation platform such as Langfuse, LangSmith, Braintrust, Arize, or Humanloop
- Strong Python and backend fundamentals
- Ability to measure and optimize latency, cost, and quality
- Experience with search infrastructure such as Elastic, Algolia, Qdrant, or Weaviate
- Experience with fine-tuning
- Experience with graph search
- Knowledge of e-commerce operations
What We Do
TextYess provides an AI-powered conversational commerce platform for eCommerce and B2C brands. Its AI agents engage customers on-site, through WhatsApp, and by voice, drawing on commerce and customer data to personalize interactions. The platform is designed to turn conversations into revenue while selling, supporting, and retaining customers autonomously. It also supports WhatsApp replies and automations such as abandoned-cart recovery, helping merchants improve customer engagement and sales.







