# Full-Stack AI Engineer — Semantic Search + Next.js + Supabase (Long-Term, Contract-to-Hire) ## About We're building an AI-native platform that makes a large archive of recorded talks genuinely discoverable and useful: need-based semantic search over transcribed media, with a subscription product built around it. We have a clear product vision and architecture and are looking for a lead engineer to build the first version and grow with us long-term. Full product details are shared with shortlisted candidates under NDA — this post focuses on the engineering and the skills we need. ## The engineering challenge You'll build a two-part system that shares one database: 1. **A content pipeline (Python):** ingest recorded talks, transcribe them, chunk and enrich the transcripts with metadata using an LLM API, generate embeddings, and store everything in Postgres. 2. **A web app (Next.js):** fast, crawler-friendly, SEO-strong content pages with structured data; retrieval-based search that returns relevant source material with links/citations; user accounts; and Stripe-gated paid content. We care a lot about retrieval *quality* and clean, maintainable architecture — this is a real product, not a prototype. ## Required tech stack - **App:** Next.js (App Router), TypeScript, Vercel. Strong SSR/SSG, SEO, and JSON-LD structured-data experience. - **AI/backend:** Python; production RAG (embeddings, chunking, retrieval quality); LLM API integration. - **Data:** Postgres + **pgvector** (via Supabase); embeddings via a hosted model (Voyage/OpenAI). - **Auth & gating:** Supabase Auth with row-level security. - **Payments:** Stripe (subscriptions + one-time). ## Required skills - Shipped production Next.js (App Router) + TypeScript apps with strong SSR/SEO. - Built a real RAG / vector-search system in production — not a tutorial clone. - Comfortable in Python for data pipelines. - Postgres + pgvector and Supabase in production. - Stripe integration. - Plans before building; communicates clearly in writing. ## Nice to have - Audio/video transcription experience (Whisper / faster-whisper / Deepgram / AssemblyAI). - Agentic coding workflows (e.g., Claude Code). - Content-heavy SEO products or media libraries. ## Engagement - Hourly, contract-to-hire. ~20–40 hrs/week to start; long-term for the right person. - We start finalists on a **small paid test project** (a single self-contained slice of the pipeline) before the full engagement — that's how we evaluate fit. ## Confidentiality This is a proprietary product. Shortlisted candidates sign a mutual NDA before we share full scope and context. Please don't expect complete product details in the first exchange — strong technical applicants will have everything they need to be evaluated, and the rest follows the NDA. ## How to apply Applications that skip these are ignored: 1. **Start your proposal with the word `pgvector`** so we know you read this. 2. Link **two** projects: one live Next.js/SSR app, and one RAG/embeddings or LLM-integration project. Tell us what *you* personally built. 3. Answer briefly: *An offline embedding pipeline and a live search query must use the same embedding model — why does that matter, and how would you guarantee it?* 4. One line on your approach to chunking long-form audio/video transcripts for good retrieval.

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.

Type: Hourly, ongoing (part-time to full-time, room to grow) Stack you'll work in: Notion, Slack, HubSpot, Google Workspace/Gmail, Claude + other LLM APIs, Zapier/Make/n8n About us We're a fast-moving sports and fan-engagement startup. We're small, we ship quickly, and we want AI woven into how the whole company operates, not as a side experiment, but as the default way we work. You'd be the person who makes that real. What you'll do Map our current workflows across sales, marketing, ops, and content, then find the highest-leverage places to automate. Build automations and agent workflows that connect our tools (Notion, Slack, HubSpot, Gmail/Google Workspace) using platforms like Zapier, Make, or n8n plus LLM APIs. Design and ship AI agents for real jobs: lead routing and CRM enrichment, content drafting, customer/fan response triage, internal knowledge search, reporting digests. Stand up the connective tissue (prompts, integrations, guardrails, and monitoring) so automations are reliable, not brittle demos. Train and enable our team: build SOPs, run working sessions, and create lightweight docs so non-technical people actually adopt what you build. Help set our AI strategy and roadmap as we scale. You're a strong fit if you Have shipped real automations and AI agent workflows in production (not just prototypes). Are fluent with Zapier / Make / n8n and at least one major LLM API (Anthropic/Claude, OpenAI). Know your way around HubSpot, Notion, Slack, and Google Workspace integrations and APIs. Can write clean prompts and think in systems: edge cases, error handling, human-in-the-loop checkpoints. Can explain technical work to non-technical people and get them to adopt it. Communicate proactively and move fast without breaking trust on things that touch customers or revenue. Nice to have Experience taking a small company "AI-native" end to end. Background in sports and/or blockchain. Comfort with light scripting (Python/JS) when no-code hits its limits. How to apply In your proposal, please: Describe one AI agent or automation you built, the tools involved, and the measurable result. Tell us how you'd approach training a non-technical team to actually use what you build. This part matters as much as the build. Share your hourly rate and weekly availability. Proposals that skip these will be passed over. We're looking to start with a small paid task and grow the engagement from there.

Project Overview We are launching a CAPDB (Customer and Prospect Database) program: a single, scored database of every current customer and every target prospect in our market. The CAPDB consolidates CRM, billing, product usage, and third party firmographic data into one master account database, applies AI assisted enrichment to fill and validate attributes, and scores every account against the profile of our best customers. You will be a dedicated hands-on builder for this program, working alongside our internal business systems and data team and reporting to the Director of IT and Business Systems. Scope of Work • Data model design. Design and build the CAPDB schema (master account, contact, and activity layers) in our Azure SQL / Microsoft Fabric environment. • Ingestion and consolidation. Build and maintain pipelines that consolidate data from our internal systems. • Entity resolution. Match, merge, and deduplicate account records across sources into a clean master record with clear survivorship rules. • AI enrichment. Build LLM assisted enrichment workflows to append, standardize, and validate firmographic attributes such as firm size, practice areas, geography, and tech stack. • Account scoring. Partner with GTM stakeholders to implement a scoring and tiering model (for example Ideal / Emerging / Acceptable / Avoid) based on fit and historical performance signals such as retention, expansion, and deal velocity. • Salesforce activation. Push scores, tiers, and enriched attributes back into Salesforce so sales and marketing can act on them in their daily workflows. • Documentation and handoff. Document the data model, pipelines, and scoring logic so our internal team can own the platform long term. Required Qualifications • 5+ years in data engineering or analytics engineering roles • Expert SQL and hands-on experience with Azure SQL Database • Microsoft Fabric experience (Data Factory pipelines, Lakehouse or Warehouse, semantic models), or deep experience with the equivalent Azure Synapse / Power BI stack • Working knowledge of the Salesforce data model and Salesforce data integration • Python for pipeline and enrichment work • Proven experience with data consolidation, master data management, or entity resolution across multiple systems • Strong written communication; able to work independently with a distributed team Nice to Have • Experience calling LLM APIs (Anthropic Claude, Azure OpenAI) for data enrichment or classification at scale • Familiarity with any of our surrounding stack: Fivetran, Chargebee, Pendo, Gong, ZoomInfo • Power BI report development and DAX • B2B SaaS RevOps or GTM analytics background; ICP definition, account scoring, or CAPDB style projects

I'm looking for an experienced AI automation engineer to build an MVP for an internal AI-powered M&A Deal Intelligence platform. Project Goal I want to build a system that helps my M&A advisory firm identify acquisition opportunities, research businesses and owners, and prepare me for outreach—all while integrating with our existing Zoho CRM. MVP Requirements - Find businesses from selected public sources using lawful methods. - Gather company information (website, industry, location, products/services, leadership, estimated size, etc.). - Research owners and decision-makers using publicly available sources (company websites, LinkedIn, news, press releases, interviews, social media, public business records where appropriate, etc.). - Generate an AI briefing before I contact a prospect that includes: - Company summary - Owner background - Personalized conversation starters - Suggested questions - Potential acquisition opportunities - Risks or notable findings - Automatically create or update records in Zoho CRM with the research and AI summaries. - Be designed so additional data sources, automations, and AI capabilities can be added over time. Preferred Skills - AI agents - Python - OpenAI API (or similar LLMs) - Web scraping - Playwright - Apify - n8n or similar automation platforms - API integrations - Zoho CRM integrations - Experience building AI research or deal sourcing tools is a major plus. When Applying Please include: - Examples of similar AI automation or research systems you've built. - The technology stack you'd recommend. - How you'd approach this project. I'm looking for a long-term technical partner to continue expanding this into a comprehensive AI platform for our M&A advisory business if the initial project is successful.

Job Title Real-Time Short-Selling Trading Bot (DAS Trader + CenterPoint Securities API) with MACD Signal & Live Dashboard Project Overview I'm looking for an experienced trading systems developer to build a real-time, low-latency short-selling bot that integrates with DAS Trader Pro and the CenterPoint Securities API. The bot needs to scan for morning gappers, confirm entries using a MACD signal, automatically locate short shares, execute trades with stop-loss and profit target orders, and display everything on a live dashboard. This is a serious algo-trading project — no polling delays, no lag between signal and execution. Core Requirements 1. Gap Scanner Scans the market in real time (pre-market/open) for gapping stocks based on configurable criteria (% gap up, float, volume, price range, relative volume, etc.) Filters results continuously as new candidates appear, not on a fixed delay 2. MACD Signal Logic Calculates MACD (configurable fast/slow/signal periods) on live price data Triggers short-entry signals based on MACD crossover/divergence rules (exact logic to be discussed/refined together) 3. Share Locate Automation Integrates with CenterPoint's locate/short-availability API to automatically source and reserve short shares before entry Handles locate failures/rejections gracefully (skip, retry, or alert) 4. Trade Execution Sends short-sell orders through the DAS Trader / CenterPoint API with minimal latency Automatically attaches stop-loss and profit-target (bracket-style) orders on fill Configurable position sizing and risk parameters per trade 5. Real-Time Dashboard Live view of: active scanner results, MACD status per ticker, current positions, P&L, order status, and locate availability Must update in real time (WebSocket/streaming preferred) — no manual refresh or noticeable delay Clean, readable UI (web-based dashboard preferred, but open to desktop app if performance is better) Ideal Skills & Experience Prior experience with DAS Trader API and/or CenterPoint Securities API (required — please confirm in your proposal) Experience building low-latency trading systems or algo trading bots Strong background in Python and/or C#/.NET (or your preferred stack, if it meets latency needs) Experience with real-time data streaming (WebSockets, market data feeds) Understanding of short-selling mechanics, locates, and day-trading order types Front-end skills for building a responsive real-time dashboard (React, or similar) Bonus: experience with other broker/trading APIs (IBKR, Lightspeed, etc.) or existing MACD/technical indicator libraries Deliverables Fully functional bot connected to live DAS Trader / CenterPoint accounts Configurable settings for scanner criteria, MACD parameters, position sizing, and risk (stop/target) Real-time dashboard (web or desktop) Documentation on setup, configuration, and how to safely test in a sim/paper environment before going live Basic logging/audit trail of trades and signals for review How to Apply Please include in your proposal: Relevant experience with DAS Trader and/or CenterPoint Securities API (links to past work if possible) Your proposed tech stack and why How you'd approach minimizing latency between signal → locate → execution Estimated timeline and cost breakdown (phased is fine — e.g., scanner/MACD first, then execution, then dashboard) Notes Will require close collaboration to finalize exact MACD entry/exit rules and risk parameters Testing must happen in a paper/sim account before any live capital is used Long-term maintenance/iteration work may be available for the right fit

We are an early-stage technology startup focused on building innovative solutions that help organizations work smarter, make better decisions, and accelerate business growth through modern technology, automation, and data-driven innovation. We're assembling a small, high-performing founding team of talented professionals who are passionate about solving complex problems, building scalable products, and creating exceptional customer experiences. We're currently seeking experienced contract professionals who are excited about working in a fast-paced startup environment where their expertise will have a direct impact on the company's growth and success. This is an opportunity to collaborate with a talented team, contribute to meaningful projects, and help shape the future of an innovative technology company. Contract Opportunities Operations Executive Assistant Finance & Operations Manager Product & Engineering Product Manager Technical Lead / Founding Software Engineer Full Stack Software Engineers (2 Positions) AI Engineer Data Engineer UI/UX Designer QA Automation Engineer Customer & Growth Customer Success Manager Enterprise Account Executive Business Development Representative Growth Marketing Manager What We're Looking For We are looking for professionals who are innovative, collaborative, and driven by curiosity. Our ideal team members enjoy solving challenging problems, thrive in dynamic environments, embrace emerging technologies, and are committed to delivering outstanding results. As a member of our founding team, you'll have the opportunity to help shape our products, influence key decisions, and contribute to building a strong culture centered on innovation, accountability, and continuous learning.

I need software engineer who can collaborate for AI training works.

Building a machine learning platform for an insurance related product, with a focus on data pipelines, feature and label design, model development, deployment planning, monitoring, and business impact. Looking for an experienced MLOps or applied ML engineer to provide weekly mentorship through structured project check ins. The implementation will remain entirely my responsibility. Each meeting will focus on the current state of the project. I will provide detailed context on what has been completed, the decisions being considered, current blockers, and the next stage of work. The mentor will review that specific situation, challenge assumptions, identify gaps, and provide direct feedback based on how the issue would be handled in a real production environment. The goal is not general instruction. Feedback should be specific to the project, its architecture, data, constraints, and business use case. Meeting Structure: - Approximately 60 minutes per week - Review of progress since the previous meeting - Discussion of current technical and business decisions - Review of architecture, pipelines, model design, or deployment planning - Identification of risks, missing requirements, and unnecessary complexity - Clear recommendations and next steps for me to complete independently Scope of Guidance: - Business use case and ROI analysis - Data architecture, quality, lineage, and source contracts - Record grain, joins, and entity matching - Feature engineering and leakage prevention - Label and outcome design - Model evaluation and business metrics - Experiment and dataset versioning - Batch and real time deployment - Monitoring, drift, retraining, and rollback - Reliability, privacy, security, and governance Work Expectations: This is a meeting based mentorship role only. The mentor will not be expected to: - perform implementation work outside scheduled meetings - write or maintain the codebase - prepare separate reports or deliverables between meetings - manage the project - provide ongoing asynchronous support - take ownership of delivery Required Experience: - Professional experience building or operating production ML systems - Strong understanding of MLOps, data engineering, deployment, and monitoring - Experience reviewing real technical systems and making practical recommendations - Ability to explain tradeoffs clearly and give direct, specific feedback - Willingness to challenge weak decisions rather than provide generic advice Working Style: Direct communication and practical feedback are important. I will prepare the project context and questions before each meeting, complete the work independently afterward, and return with results for review. Initial Engagement: The engagement will begin with one paid consultation. The session will be used to review the project, discuss the expected mentorship style, and determine whether recurring weekly meetings are a good fit.

AI SYSTEMS ENGINEER Agentic AI, Multi-Agent Systems & Secure AI Workflows (U.S.) Remote • United States We're building production AI systems designed for enterprise environments. We're looking for exceptional AI systems engineers who enjoy solving difficult systems problems – not just writing code. Our work sits at the intersection of agentic AI, software architecture, enterprise systems, governance, security, and operational intelligence. We design AI systems that improve how organizations operate while meeting the standards required for production deployment. We value engineers who think in systems, challenge assumptions, and care deeply about building technology that is reliable, understandable, secure, and useful. If you're motivated by difficult engineering problems, thoughtful architecture, and building production AI systems for enterprise organizations, we'd like to hear from you. WHAT YOU'LL HELP BUILD Examples of the types of systems we design include: - Multi-agent AI systems - Enterprise AI assistants - Secure AI workflows - Enterprise workflow automation - AI-powered knowledge systems - Human-in-the-loop decision support - Document intelligence - Retrieval-Augmented Generation (RAG) - AI memory and retrieval systems - AI evaluation and testing frameworks - Secure enterprise AI platforms - AI governance capabilities - Operational intelligence platforms TECHNICAL EXPERIENCE WE VALUE We're interested in engineers with experience in some combination of: - Python - AI Agent Development - LangGraph - LangChain - Large Language Models - API Development - Vector Databases - Software Architecture - Enterprise Systems Integration - Information Security Experience with OpenAI, Anthropic, Model Context Protocol (MCP), cloud infrastructure, workflow orchestration, observability, distributed systems, or regulated technology environments is also valuable. We do not expect expertise in every technology. We care far more about engineering judgment, systems thinking, demonstrated execution, and continuous learning than checking every technology box. THE PROBLEMS WE ENJOY SOLVING The engineers who thrive here enjoy questions like: - How should multiple AI agents coordinate work? - How should humans remain in control of important decisions? - How should production AI systems scale safely? - How should memory be designed for enterprise AI? - How should AI systems balance operational performance with governance, security, and reliability? - How should AI systems create measurable business value? If those questions excite you, you'll probably enjoy working with us. WHAT MAKES SOMEONE SUCCESSFUL HERE We're looking for engineers who: - Think in systems rather than individual features. - Care deeply about production quality. - Enjoy solving ambiguous technical problems. - Communicate complex ideas clearly. - Balance speed with sound engineering judgment. - Build practical solutions rather than chasing hype. - Continuously learn, experiment, and improve. We're significantly more interested in systems you've built than technologies you've used. Please provide specific examples that demonstrate your role, engineering decisions, and measurable outcomes. We recognize that many engineers use AI as part of their workflow. You're welcome to do the same. However, your application should accurately reflect your own experience, judgment, and technical thinking. We respect the confidentiality of your current and former employers, clients, and partners. Please do not include proprietary or confidential information in your application. Describe your work at a level that demonstrates your engineering approach without disclosing protected information. PROFESSIONAL STANDARDS We value integrity, sound engineering judgment, and respect for intellectual property. Please do not include confidential, proprietary, export-controlled, or other non-public information belonging to your current or former employers, clients, or partners in your application or work samples. We're interested in your engineering approach, architectural thinking, and problem-solving methodology – not protected information belonging to others. If you share code, architecture diagrams, technical documentation, or project examples, please ensure you have the legal right to do so and identify any material open-source or third-party technologies where appropriate. By submitting application materials, you represent that you have the legal right to share them and that doing so does not violate any confidentiality, intellectual property, employment, consulting, or other contractual obligations. Any engagement, if offered, will be subject to a separate written agreement covering confidentiality, intellectual property ownership, compensation, and other applicable terms. Submission of an application or participation in the evaluation process does not create any employment, independent contractor, partnership, joint venture, agency, fiduciary, or other business relationship with 26ers AI, nor does it obligate either party to enter into any future engagement. 26ers AI reserves the right to evaluate applications, discontinue discussions, modify the hiring process, or decline to pursue any engagement at its discretion. Nothing in this posting should be construed as an offer of employment or an offer to contract.