Founding AI Full-Stack Engineer – B2B Sales Intelligence SaaS Platform

We are building an AI-powered B2B Sales Intelligence SaaS platform that helps businesses identify high-potential accounts, understand buying signals, discover relevant decision-makers and generate actionable sales intelligence. We already have documented business workflows, product requirements and a detailed Business Requirements Document (BRD). We are looking for a hands-on Founding AI Full-Stack Engineer / Technical Lead who can translate these requirements into a functional software product. This is an opportunity to take ownership of the initial technical architecture, build the first working version and potentially lead the engineering team as the product grows. 1. What You'll Build The platform is designed to: Capture business signals such as company expansion, funding, acquisitions and leadership changes. Identify and match signals with the correct companies. Analyse business developments using AI and LLMs. Score accounts against customer-defined Ideal Customer Profiles (ICPs). Identify relevant decision-makers and map buying committees. Generate personalised outreach drafts and account intelligence briefs. Enable human review, approval and CRM handover. Customers will be able to configure their target profiles, scoring rules and workflows through individual workspaces. Important product requirement: The platform must distinguish between verified facts, AI-generated inferences and information actually confirmed by buyers. Evidence, source attribution and confidence scoring are essential. 2. Key Responsibilities A. Software Architecture & Backend Engineering Review the existing BRD, workflows and product requirements. Design the application architecture and technical roadmap. Build backend services, REST APIs and business logic. Design relational databases and data models. Establish a scalable multi-tenant SaaS architecture. Implement authentication, role-based access controls and workspace management. Make technology decisions covering what to build, buy or integrate. B. AI & LLM Engineering Develop AI workflows using OpenAI, Anthropic Claude or equivalent LLM APIs. Implement prompt engineering, structured outputs and context management. Build AI-powered account research and opportunity assessment. Implement evidence validation and source attribution. Handle conflicting information, weak signals and incomplete data. Build evaluation frameworks to measure AI accuracy, consistency, latency and cost. Implement retry mechanisms, fallback logic and failure handling. C. Data Engineering & Intelligence Pipelines Build data ingestion and processing pipelines. Integrate external business data providers and APIs. Implement company matching, entity resolution and deduplication. Develop contact enrichment and verification workflows. Maintain source references, timestamps and confidence scores. Build scheduled data refresh and incremental update processes. Evaluate data provider reliability, coverage and commercial costs. D. Full-Stack Product Development Develop frontend interfaces using React, Next.js and TypeScript. Build customer workspaces and account intelligence dashboards. Create ICP configuration and account scoring interfaces. Develop signal review and buying committee views. Build human approval interfaces for outreach drafts and intelligence briefs. Implement CSV exports and CRM handover functionality. Ensure a responsive and user-friendly product experience. E. Cloud Infrastructure, DevOps & Security Recommend and implement cloud infrastructure. Set up application environments and CI/CD pipelines. Implement logging, monitoring and error tracking. Establish secure API integrations and secrets management. Implement appropriate data access controls and audit logging. Design for reliability, performance and future scalability. F. Technical Leadership Define engineering standards and development practices. Identify technical capabilities required for the next development phase. Prepare technical job descriptions and role specifications. Evaluate engineering candidates through technical assessments and interviews. Review code, architecture and engineering deliverables. Lead and mentor engineers or contractors joining the project 3. Required Technical Skills Technical Area Preferred Technologies Backend Python, FastAPI, Node.js Frontend React, Next.js, TypeScript Database PostgreSQL, SQL, Redis AI & LLM OpenAI API, Anthropic Claude AI Frameworks LangChain, LangGraph or equivalent Data Engineering ETL/ELT, data pipelines, entity resolution Vector Search pgvector, Pinecone, Weaviate APIs REST APIs, webhooks, CRM integrations Cloud AWS, Azure or Google Cloud DevOps Docker, CI/CD, GitHub Actions Security Authentication, RBAC, multi-tenant security Candidates do not need experience with every technology listed. Strong engineering fundamentals and the ability to select the right architecture are more important. 4. Who Should Apply? We are looking for professionals with experience as: Founding Engineers. Senior Full-Stack Engineers. AI SaaS Product Engineers. Hands-on Technical Leads. Technical Co-founder-type professionals. The ideal candidate should: Have personally designed, developed and launched at least one B2B SaaS, AI SaaS or data-intensive software product. Demonstrate strong backend development and system architecture capabilities. Have practical experience deploying AI/LLM workflows into production. Understand data engineering, APIs and third-party integrations. Be comfortable developing functional frontend interfaces. Have experience with cloud infrastructure and production deployment. Be capable of making independent technical decisions. Important: This is a hands-on engineering engagement, not an advisory-only or project management position. You must be comfortable writing code, building the first working version and taking direct technical ownership. 5. Initial Paid Project – 3–4 Weeks The initial engagement will focus on delivering a functional technical prototype and establishing the product's technical foundation. Deliverable 1: Functional Software Prototype Build a working prototype using five agreed sample accounts. The prototype should demonstrate: Business signal capture with source references and timestamps. Company identification and matching. AI-driven opportunity assessment. Account scoring against defined ICP criteria. Decision-maker and buying committee mapping. Personalised outreach draft generation. Account intelligence brief generation. Human review and approval workflow. CSV export for CRM handover. The prototype must also demonstrate how the system handles at least one weak signal, conflicting evidence or ambiguous company match. Deliverable 2: Technical Architecture & Development Roadmap Provide a practical technical plan covering: Recommended application architecture and technology stack. Backend, frontend, database and cloud infrastructure. AI models, orchestration and evaluation approach. Recommended business data sources and API integrations. Data ingestion, matching, enrichment and verification processes. Security, access controls and multi-tenant architecture. Development milestones and estimated timelines. Estimated infrastructure, AI API and external data provider costs. Estimated cost of researching and refreshing accounts. Deliverable 3: Engineering Hiring Plan Recommend the next one or two technical hires or contractors required to move from prototype to paid pilot. For each role, define: Job title and responsibilities. Required technical skills and experience. Specific modules or components they will own. Technical assessment requirements. Interview evaluation criteria. Recommended hiring sequence. 6. Project Milestones The initial project will be structured around agreed, funded milestones. Milestone Scope Milestone 1 Technical discovery, architecture and development setup Milestone 2 Functional AI prototype and core product workflows Milestone 3 Testing, documentation, technical roadmap and hiring plan Detailed acceptance criteria, timelines and milestone amounts will be agreed before the contract starts.