AI/Full-Stack Engineer for Sales Chatbot

AI / Full-Stack Engineer – Build a Sales Enablement Chat Assistant We are hiring an experienced AI / full-stack contractor to help lead the build of a production-grade, sales-focused chatbot that integrates natural language interaction with structured enterprise data. This is not a generic chatbot. The goal is to create a domain-specific sales enablement assistant that allows business users (sales, account management, leadership) to ask questions in natural language and receive accurate, grounded responses from multiple internal data sources. The system must balance usability, reliability, trust, and real business value, with a strong emphasis on intent classification, data grounding, and actionable outputs. We have already completed initial planning, defined milestones, and established technical direction. We’re looking for someone who can step in and drive implementation of the chatbot layer, especially during the core build phase. ________________________________________ What You’ll Work On • Designing and building a production-ready chatbot experience focused on sales workflows • Enabling users to ask questions such as: o customer insights o product availability / catalog data o pricing / contract context o sales performance / pipeline-related queries • Building intent classification and routing logic to determine: o what the user is asking o which data source(s) to query o which tools or workflows to trigger • Connecting LLM behavior to multiple structured and semi-structured internal data sources • Defining architecture for: o prompt flow o tool usage and orchestration o retrieval and routing across sources o answer grounding and validation • Improving response quality, accuracy, and trustworthiness for business users • Helping move from MVP/prototype to a more launch-ready system • Collaborating closely with internal product and technical stakeholders ________________________________________ Core Areas of Focus • LLM application architecture for multi-source enterprise data environments • Intent classification and query routing strategies • Tool-calling / MCP-style integrations • Retrieval and semantic layer design across different datasets • Prompt and system instruction design at scale • Grounding strategies and answer quality optimization • Chat UX thinking (working alongside a separate UI refresh effort) • Logging, monitoring, and production readiness • Evaluating tradeoffs across speed, cost, security, and maintainability ________________________________________ Ideal Experience • Proven track record building AI assistants or chat-based tools for sales, customer-facing, or operational workflows • Experience working with multiple data sources (CRM, ERP, product/catalog, analytics, etc.) • Hands-on experience with intent classification, routing, and orchestration in LLM systems • Strong experience connecting LLMs to structured enterprise data and internal systems • Focus on production reliability, edge cases, and real-world usage, not just demos • Ability to think in systems and architecture, not just prompt engineering • Comfortable working in ambiguity and helping shape execution while building • Full-stack capability is a plus (especially bridging backend orchestration with frontend chat experience) ________________________________________ Project Context • Part of a broader product initiative with multiple parallel workstreams • Internal team is already covering: o UI refresh o related automation efforts • Your focus will be the AI/chat experience, especially: o intent classification o multi-source data integration o response orchestration • Some details will be shared later due to project confidentiality ________________________________________ What Success Looks Like • A working, sales-enablement chatbot that business users actually rely on • Accurate responses grounded across multiple trusted data sources • Effective intent classification and routing system • Clear, scalable technical architecture for: o querying o reasoning o response generation • Meaningful progress against defined milestones • Strong partnership with internal leads (not just ticket execution)