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About Us Paragon International, Inc. is a U.S.-based manufacturer of commercial concession equipment and food service products. We receive purchase orders from customers such as Amazon, Home Depot, distributors, school systems, and other commercial customers. Orders arrive by email in many different formats, including PDFs, Word documents, Excel spreadsheets, scanned documents, and occasionally photographed purchase orders. We are looking for an experienced AI Automation Engineer to design and build a production-ready system that automates our entire order intake process. This is not a simple chatbot project. We need someone who has successfully built business automation systems that combine AI, OCR, document processing, APIs, and workflow automation. Project Overview The system will monitor one or more Gmail inboxes continuously and automatically process incoming emails and attachments. The workflow should: * Monitor Gmail 24/7 for new incoming emails. * Download all attachments automatically. * Read: * PDF files * Microsoft Word documents * Excel spreadsheets * Scanned PDFs * Image files (JPG, PNG, TIFF, etc.) * Photographs of purchase orders * Use OCR when required. * Use AI to determine whether the email is: * Purchase Order * Quote Request * Cancellation * Return/RMA * Customer Inquiry * Other * Identify the customer automatically. * Extract all order information into a standardized data structure. * Detect duplicate purchase orders. * Automatically print valid purchase orders to our network printer. * Save documents into organized folders. * Rename files using a consistent naming convention. * Move processed emails into Gmail folders/labels. * Generate logs for auditing and troubleshooting. ## Future Phases The initial project focuses on reliable document processing and printing. Additional phases may include: * Sage 100 ERP integration * Automatic sales order creation * Inventory verification * Customer acknowledgment emails * Shipping workflow automation * Dashboard and reporting * AI exception handling * Multi-location printing We are looking for a long-term development partner who can continue improving the system over time. ## Required Skills Please apply only if you have strong experience with most of the following: * OpenAI API / ChatGPT API * Gmail API * OCR technologies (Tesseract, Azure Document Intelligence, Google Vision, AWS Textract, or similar) * Intelligent Document Processing (IDP) * PDF parsing * Workflow automation * Python * REST APIs * Windows automation * Network printing * Error handling and logging * AI document classification Experience with the following is a significant advantage: * n8n * Microsoft Power Automate * Make.com * ERP integrations * Sage 100 * Purchase Order processing * Manufacturing or distribution businesses ## Deliverables The completed solution should: * Run continuously with minimal supervision. * Be reliable enough for production use. * Handle errors gracefully. * Be well documented. * Be easy for our staff to maintain. * Be scalable as our order volume grows. ## To Apply Please include: 1. A description of similar automation projects you have completed. 2. Which automation platform you recommend (Python, n8n, Power Automate, Make, or another solution) and why. 3. Examples of AI document processing or OCR projects you've built. 4. Your experience integrating with ERP systems. 5. Your estimated timeline. 6. Your hourly rate or fixed-price proposal. Please begin your proposal with the phrase: **"I have built AI document automation systems."** This helps us identify applicants who have carefully read the project description. We are looking for a long-term partner, not just someone to complete a single project. If this project is successful, additional work will include ERP integration, warehouse automation, customer service automation, purchasing automation, and AI-driven business process improvements.

We need a senior architect to design and build a multi-model routing control plane, then lead a small senior team through the build. The control plane sits in front of a family of AI systems and decides, per request (text, image, video), the optimal path across cost, quality, latency, business value, and sovereignty (data residency, rights, and cultural fit): cache and reuse, a small or on-device model, an open-weight, fine-tuned, or sovereign model, or a higher-cost frontier fallback. It routes across compute too: CPU, GPU, inference accelerators, on-device, and edge. Core KPIs: the share of eligible workload kept off frontier accelerators and the resulting cost reduction on a representative workload, plus sovereignty compliance, with no quality regression. This is not a chatbot and not a wrapper over hosted APIs. You own the architecture, define the routing logic, and lead execution. You think in systems, not individual model calls. Context The router is one component of a larger AI platform. It must be model-agnostic: open-weight, fine-tuned, and proprietary models swap in and out behind a stable interface without rearchitecting. A separate team owns the models you route to. The engagement is a 60 to 90 day POC with a working router demo (text-first, with a defined path to image and video), followed by technical leadership through the build. What you'll own Control plane: intake and normalization, classification, routing taxonomy, model-selection logic, fallback hierarchy, cache and reuse rules, telemetry, and the eval feedback loop. Routing that is learned and calibrated, not just static rules: predict per-query difficulty and expected quality, and escalate on confidence thresholds. Comfort with cascades and speculative decoding is expected. Routing across cost, quality, latency, and policy. In constrained environments some requests must stay local regardless of cost. Model-agnostic interface: clean, stable contracts so models and execution paths swap without rework, and the separate model team can work independently of the routing layer. Cost optimization across compute: exact and semantic cache, prefix/KV cache reuse, output reuse, batching, small-model routing, CPU offload, and on-device/edge execution, with a clear fallback hierarchy. The goal is to move most eligible workload off frontier accelerators without degrading output. Generative caching and reuse: caching text is easy; image and video are not, since the same prompt should produce variation rather than an identical result. We need credible reuse at the asset or component level, not just for text. Eval loop: scores output quality by domain and flags weakness so the training team can target fixes instead of retraining broadly. Track quality vs intent, failure modes, cost per route, latency per route, cache hit rate, fallback rate, and regeneration rate. Execution and leadership: architecture blueprint, POC scope, milestones, infra assumptions, and risks leadership can review, plus hands-on architecture review and task breakdown. You'll lead a small senior team, and one of your first deliverables is recommending its exact composition (see screening questions). Ideal background Led or architected production AI infrastructure across several of: multi-model orchestration and LLM routing, multimodal, model serving, inference cost and GPU reduction, CPU and on-device inference, open-source and fine-tuned deployment, cascades and speculative decoding, semantic and prefix caching, eval pipelines, and AI observability. Deployed in at least one constrained environment: on-prem, self-hosted, air-gapped, or data-residency-restricted. You know what breaks when you can't lean on a single cloud. Can lead: set architecture, break down work, review the team's output, and keep the build on track. Tools matter less than the ability to architect the system correctly and lead execution. Not a fit: basic chatbot workflows, hosted APIs only, or prompt engineering alone. Deliverables Control plane blueprint, routing taxonomy, POC plan with milestones and success criteria, and an eval/feedback framework, with a working router demo as the 60 to 90 day target, then technical leadership of a small team through the build. Screening questions The most relevant AI routing, model-serving, or inference infrastructure system you personally designed or built: what was routed, which models or execution paths, and what role did you own? How would you design a router that chooses between cache/reuse, a smaller or local model, an open-weight or fine-tuned model, or a frontier fallback, across CPU and GPU? Where do learned routing, cascades, or speculative decoding fit? For generative image or video requests, how would you approach caching or reuse when the same prompt should still allow variation? Be specific. What metrics and eval loop would you use to prove the router cuts cost without degrading quality, and to help a separate training team find weaknesses? Beyond yourself, what team would you staff to hit these deliverables in 8 weeks? Give the roles, seniority, and headcount, how you'd split the work, and flag any deliverable that 8 weeks and a team of roughly 4 engineers can't realistically cover. To apply Answer the five questions, summarize your most relevant routing or inference-infrastructure work (repos, writeups, talks, or architecture you can describe), and give your high-level approach to a control plane that routes across cost, quality, and sovereignty while preserving quality. Note your availability, your rate, whether you've led a small engineering team before, and the team you'd staff to hit the deliverables in 8 weeks.