AI Solutions Architect

Our company is building an automation system for procurement and sales of aircraft components. The system automates the full RFQ cycle: receiving requests from the ERP, selecting suppliers, sending requests, processing replies with AI, generating quotes, and preparing commercial proposals for customers. The solution is already operating on real RFQs and commercial data. The next step is moving from automating individual operations to a governed decision\-making system that can independently handle a significant share of standard transactions and route only complex, high risk, and non standard cases to humans. **The role** ------------ We are looking for a hands on AI Solutions Architect to design the target architecture and help transform the existing working product into a scalable, governed platform for procurement and sales automation. This is not a purely advisory role. The architect must understand the existing codebase, participate in prototyping critical components, run architectural reviews, and work with the engineering team to bring solutions to production. The primary outcome of the role is not architecture documentation per se, but a working system that increases RFQ processing speed, reduces manual operations, and delivers measurable operational and commercial impact for the customer. **Responsibilities** ==================== * **Product architecture** * Audit the current architecture Define the target architecture for the full cycle: RFQ Quote Sales Order Delivery* Close * Set clear boundaries between business rules, algorithms, statistical models, and AI agents * Prepare a phased technical roadmap for evolving from the current solution to an autonomous engine * Design a scalable, maintainable architecture to replace the current monolithic setup * Define requirements for data, logging, observability, fault tolerance, and security * **AI and decision automation** * Design AI‑agent workflows for supplier requests, web search, and retrieval of up‑to‑date prices * Define architecture for using LLMs, tools, confidence scoring, and human‑in‑the‑loop * Develop exception‑handling rules, including AOG cases, high deal value, and low model confidence * Determine where AI is truly needed versus where formal rules are more reliable * Contribute to the development and testing of prompt logic, extraction pipelines, and agent orchestration * **Pricing and decision‑making** * Design a Pricing Engine * Define the architecture for calculating margin, deal probability, supplier risk, and commercial attractiveness * Separate rule‑based, statistical, and ML approaches * Plan the use of historical data from eMARS and other price sources * Define a model for selecting the best price factoring recency, supplier reliability, lead times, and risk * **Engineering execution** * Collaborate with the lead engineer and developers * Participate in implementing critical components and prototypes * Conduct architecture and code review