Location: Hybrid (UK preferred)

Reporting to: Head of Engineering

Key Partners: Kim Faura (Product Lead) & Pravin Paratey (Head of Engineering)

The Split: 80% Deep Building & Coding | 20% Technical Leadership & Team Shielding

About SalesAPE & Abi

We are building what we believe will become the operating system for millions of small businesses.

Today, we have one main product brand — SalesApe, which helps businesses automate customer conversations, qualify incoming leads, and convert more sales. Alongside this, we are building Self-Serve Abi (Artificial Business Intelligence) — a natural language AI business partner that allows business owners to create, operate, and grow their businesses simply by talking to an AI.

But our long-term vision goes far beyond individual AI agents. We believe the next generation of software will continuously learn from the outcomes it creates. Every customer interaction, recommendation, experiment, and business outcome should make the platform smarter for the next customer. To achieve that, we're looking for a Senior Founding Engineer to build the core intellectual property that ties these products together: our unified, self-improving Learning Platform.

The Mission

Your mission is to architect and build the intelligence layer that sits behind both SalesApe and Self-Serve Abi. This platform will capture business events, measure outcomes, identify patterns, and continuously improve the recommendations our AI makes.

Rather than simply orchestrating existing foundational models, you will build a self-improving recommendation and learning engine that compounds over time. Imagine millions of businesses collectively teaching the platform: which sales techniques convert best, which marketing campaigns actually work, and which onboarding journeys reduce churn. Every customer benefits from the learnings generated by every other customer, strictly preserving privacy and security.

This is not a theoretical academic exercise. To prove the value of this platform early, you will anchor the initial learning loops onto the rich data and events we already generate, directly targeting the immediate onboarding and retention challenges we're chasing right now. This ensures the learning platform drives immediate product value while we build toward the multi-year strategic defensibility moat we need ahead of our Series B.

What This Role Actually Is (and Isn't)

We are not looking for an "ivory tower" architect or a hands-off engineering manager. We need a highly skilled, pragmatic engineer who is still deeply in love with writing code and shipping systems. The role splits into two primary responsibilities:

• 80% Engineering & Building: You will spend the vast majority of your time architecting, writing, and shipping production-ready code. You will inherit a seeded prototype of our knowledge layer and harden it into a robust, scalable, and resilient production platform.

• 20% Technical Leadership & Shielding: You will partner closely with the Senior Leadership Team to ruthlessly prioritize the technical roadmap. You will guide other engineers on architectural standards and act as a protective buffer — keeping them safe from the daily "noise" of a fast-growing startup so they can focus on deep, uninterrupted builder mode.
  

What You'll Build

You will design, own, and scale the architecture behind a continuously learning platform, including:

• Event collection architecture & customer interaction pipelines to capture rich interaction logs cleanly.

• Outcome measurement frameworks to tie AI suggestions to actual business outcomes (sales, retention, clicks).

• Recommendation & feedback loops that let the AI automatically improve its behavioral models based on real evidence.

• Knowledge graphs, vector databases, and memory/retrieval systems that serve as our persistent cross-product intelligence.

• Experimentation infrastructure & feature stores to run secure experiments and manage features efficiently.

• Evaluation frameworks to continuously benchmark and validate prompt and model improvements.
  

What Success Looks Like

Within 12 months, you will have shifted us from manual prompt tuning to an automated, compounding loop of intelligence:

• Structured Learning: Every customer interaction automatically translates into structured, usable learning data.

• Measurable Performance: Every single AI recommendation can be tracked and measured against real-world business outcomes.

• Compounding Defensibility: Every experiment run by one customer improves future recommendations for all other customers, safely and securely.

• Opinionated AI: Our AI agents become increasingly opinionated, moving beyond basic prompt rules to act on real-world evidence of what works.

• Autonomous Improvement: The platform improves continuously over time without requiring manual developer intervention.
  

Who We're Looking For

We value mindset over specific job titles. You are an exceptional systems thinker who thinks in feedback loops rather than simple product features. You naturally ask yourself: "How does this system get smarter every day?"

Ideal candidates bring experience in:

• Distributed systems, event-driven architecture, and large-scale event processing.

• Data engineering, stream processing, feature stores, and robust data modeling.

• Graph databases (knowledge graphs) and vector databases for retrieval and memory systems.

• Python, TypeScript, SQL, and modern cloud infrastructure (AWS/GCP/Azure).

• Recommendation engines, personalization platforms, or reinforcement learning pipelines.

• Designing LLM application architectures and robust AI evaluation frameworks (prior GenAI experience is highly beneficial but not strictly mandatory).