NobleProg is seeking an experienced AI Trainer to deliver a live, instructor-led remote training focused on helping technical professionals integrate Agentic AI and RAG systems into their existing workflows. This opportunity is designed for participants with strong technical backgrounds (Data Engineering and Workflow Automation) but limited formal AI experience, with the goal of applying AI to real-world systems rather than learning theory. Engagement Details Location: Remote Duration: 2 days Audience: Data Engineers and Workflow Developers Participants: 4+ Daily Rate $1,100 per day Course Scope This training focuses on practical, hands-on development of AI-powered systems using Retrieval-Augmented Generation (RAG) and agent-based architectures. The course will follow a Core & Split approach, starting with shared foundational concepts, moving into role-specific deep dives, and concluding with an integrated session demonstrating how AI systems are built and applied across workflows and data pipelines. NobleProg SOP - https://share.synthesia.io/a0788c6e-56d5-4da8-92c6-0d5c03ad6d52 Key Topics Include - Practical introduction to LLM applications and AI system architecture - Retrieval-Augmented Generation (RAG) design and implementation - Data preparation, embeddings, and vector database concepts - Agentic AI fundamentals (tools, decision-making, multi-step workflows) - Orchestration frameworks such as LangChain, LangGraph, or similar - Role-based applications: RAG pipelines for data engineers and AI-driven workflows for workflow developers - End-to-end system integration (RAG + agents + automation) Trainer Responsibilities - Deliver engaging, instructor-led remote training with strong hands-on focus - Translate AI concepts into practical applications for non-AI technical professionals - Structure delivery using a Core & Split model to address different roles - Provide real-world exercises aligned with data pipelines and workflow automation - Facilitate an integrated session demonstrating how different components work together - Prepare training materials (trainer retains ownership of content) Required Qualifications - Hands-on experience building LLM-based applications, including RAG systems and agent-based workflows - Strong proficiency in Python and experience with APIs, data pipelines, or automation systems - Experience with frameworks such as LangChain, LangGraph, or similar - Proven experience delivering technical training to engineering audiences - Ability to simplify AI concepts and connect them to real-world use cases Nice to Have - Background in data engineering, workflow automation, or solutions architecture - Familiarity with MCP or emerging agent orchestration frameworks - Experience designing modular or role-based training programs preferred - Experience building production-grade AI applications preferred https://docs.google.com/document/d/184VlJipyixkLNJ_HnP3aPt4YToedTUAlji_LxkuLhRU/edit?usp=sharing Please review and approve this tentative outline. We will be meeting with the client to determine whether they prefer a 1-day or 2-day delivery format. The agenda may require some adjustments based on the client's specific objectives, technical background, and areas of interest, which can be finalized during the trainer-client consultation call. Could you please review the proposed outline and let us know if you see any red flags, gaps, concerns, or topics that may require immediate attention? We would also appreciate any recommendations regarding scope, level of technical depth, hands-on exercises, or prerequisite knowledge that should be addressed before presenting this to the client. Thank you for your feedback. How to Apply Please include - A brief overview of your experience with Agentic AI and RAG systems - Your experience delivering technical or AI-focused training - Examples of AI systems or applications you have built - Your approach to teaching participants without formal AI background - Availability for remote delivery

Seeking an experienced AI Solutions Architect to provide consultation and design an enterprise-grade Agentic AI platform capable of automating business workflows, retrieving knowledge from enterprise data sources, and integrating with existing business systems. This engagement focuses on solution architecture, technical design, technology selection, implementation planning, and best practices rather than full-scale application development. Scope of Consultation The consultant will: Assess current business processes and identify high-value AI automation opportunities. Design an enterprise Agentic AI architecture aligned with business and technical requirements. Define multi-agent workflows, agent responsibilities, and orchestration strategies. Design a scalable Retrieval-Augmented Generation (RAG) architecture for enterprise knowledge retrieval. Recommend the appropriate Large Language Models (OpenAI, Claude, Gemini, AWS Bedrock, Azure OpenAI, etc.) based on cost, performance, and use cases. Recommend vector database technologies and semantic search architecture. Design secure integrations with enterprise applications, APIs, and internal knowledge repositories. Define prompt engineering strategies, AI guardrails, evaluation methodology, and governance practices. Recommend cloud architecture and deployment strategies for AWS, Azure, or Google Cloud Platform. Provide guidance on LLMOps, monitoring, observability, security, model lifecycle management, and scalability. Develop an implementation roadmap, including phases, estimated effort, risks, and technical recommendations. Required Expertise Enterprise AI Solution Architecture Agentic AI Multi-Agent Systems Retrieval-Augmented Generation (RAG) Large Language Models (LLMs) LangChain LangGraph Prompt Engineering OpenAI API Anthropic Claude Google Gemini AWS Bedrock Python Vector Databases Enterprise System Integration AWS Microsoft Azure Google Cloud Platform AI Governance LLMOps MLOps Workflow Automation Deliverables Enterprise Agentic AI solution architecture document Multi-agent workflow design and orchestration diagrams RAG architecture and knowledge management design Vector database recommendation and data flow architecture Cloud deployment architecture Integration strategy for enterprise systems AI governance, security, and LLMOps recommendations Implementation roadmap with milestones and estimated effort Architecture review presentation and knowledge transfer session Project Outcome Delivered a comprehensive enterprise AI architecture and implementation strategy that provides a scalable foundation for deploying Agentic AI solutions. The consultation enabled stakeholders to make informed technology decisions, reduce implementation risks, accelerate development, and establish best practices for governance, security, and long-term operational success.

I want to build a private multi-model RAG-based Opportunity Intelligence Agent. It should support document ingestion, opportunity-specific workspaces, vector search, source citations, multi-model routing across OpenAI, Claude, Perplexity, and possibly DeepSeek, and generate strategic recommendations from both uploaded files and live web research. This is intended to become a reusable base agent capable of knowledge retrieval, web research, multi-model orchestration, document analysis, citation generation, and agent clonding and configuration. It will be used for analyzing & strategy development for project opportunities, responding to RFPs, and proposal assistance, as well as other applications.

I’m building Elevyn, a platform designed to help people better understand themselves through connected data, intentional reflection, and AI-generated insights. This is not a social media platform. This is not a productivity app. This is not just another trading journal. Elevyn is an operating system for self-mastery, with trading serving as the first environment where personal growth becomes measurable. Most apps collect data. Elevyn is designed to observe. The goal is to connect information across different areas of a person’s life and uncover meaningful patterns that help them improve their decision-making, habits, discipline, and overall performance. The Vision Users will log information such as: • Sleep • Mood • Daily reflections • Workouts • Morning routines • Trading activity • Goals • Habits • Personal notes Instead of simply displaying this data, Elevyn will analyze relationships between it and surface meaningful insights. Examples include: • “You slept less than 5 hours before 7 of your last 10 losing trades.” • “Your win rate increases after completing your morning routine.” • “You tend to become more impulsive after multiple winning days.” • “Your consistency improves when workouts and journaling happen on the same day.” The long-term vision is an intelligent system that continuously helps users become more self-aware. Current Stage I already have: • Brand identity • Vision and philosophy • User flows • Interactive prototype • Core feature planning • Long-term roadmap I’m now looking for a developer or small team (2-3 people) to build the first production version. What I’m Looking For I’m looking for someone who can think beyond simply building screens. I want someone who can: • Build scalable architecture • Recommend the best technologies • Think through user experience • Ask thoughtful questions • Help solve technical challenges • Build for future AI integrations • Create clean, maintainable code Experience with AI, analytics, mobile applications, and scalable backend systems is a significant advantage. Technology (Open to Recommendations) I’m open to your recommendations, but I’m considering: • Flutter or React Native • Node.js or Python backend • PostgreSQL • Firebase/Supabase • AWS • AI integrations (OpenAI or similar) • Secure authentication and cloud infrastructure Long-Term Vision Trading is only the beginning. The underlying engine should eventually support entrepreneurs, athletes, creators, students, and anyone pursuing mastery in a specific area of life. The core idea remains the same: Help people understand themselves by connecting patterns across their behaviors, decisions, and outcomes. Who I’m Looking For I’m looking for someone who believes in building meaningful products—not just completing tasks. If you’re excited about creating a platform that combines psychology, data, AI, and personal development into something people genuinely use to improve their lives, I’d love to hear from you. Please include examples of similar work, your recommended tech stack, and how you would approach building a scalable MVP that can grow over time.

I am looking to partner with a competent dev team (individual or team) to launch AI implementation solutions for small and medium-sized businesses. As a sales professional, I aim to provide innovative solutions that enhance business operations. The ideal candidate will have experience in AI technologies and a strong understanding of business needs.

Title: AI Agent System for SaaS Platform (Product Manager, Developer, QA, Marketing Agents) Description: We are building useContractor.ai, a contractor management SaaS platform. We are looking for an experienced AI automation engineer to build a multi-agent system that helps manage, improve, and grow our application. Initial agent ecosystem: Product Manager Agent Reviews user feedback Reviews analytics Prioritizes bugs and features Creates development tasks Developer Agent Reviews GitHub repository Generates code recommendations Creates implementation plans Assists development team QA Agent Tests workflows Identifies bugs Generates bug reports Verifies fixes Executive Reporting Agent Summarizes daily activity Reports bugs, improvements, user trends, and recommendations Requirements: OpenAI or Azure OpenAI n8n or similar orchestration platform GitHub integration Analytics integration (PostHog, Clarity, etc.) Scalable architecture for future agents Future phases: Marketing Agent Customer Support Agent Sales Agent Marketplace Moderation Agent AI Estimating Agent Please provide examples of: AI agents you have built Multi-agent systems SaaS automation projects GitHub/OpenAI integrations

Overview We run an AI voice assistant for self-storage operators. We have an internal, AI-assisted workflow for triaging call feedback — investigating what happened on a call, diagnosing the root cause in our codebase, and drafting fixes. We’re looking for someone technical to run that AI-assisted workflow day to day and help us make it better. You’ll be driving AI coding agents, reading real code to understand behavior, and improving the process and tooling itself. What you’ll do Use our AI agent tooling to work through a queue of customer feedback on AI voice calls. Read our TypeScript/Node codebase (voice-agent prompt assembly, workflow/“SOP” engine, tool implementations) to diagnose why the agent behaved a certain way — not just guess. Draft fixes: workflow-instruction edits, knowledge-base entries, or code changes via pull request with a clear verification plan. Improve the triage process itself — refine the AI agent prompts/skills, conventions, and the internal MCP tooling that powers it. Write clear, customer-facing summaries of what changed for our team to review and approve. You’re a great fit if you Read and reason about code confidently — ideally TypeScript/Node; React a plus. Have hands-on experience driving AI coding agents (Claude Code, Cursor, or similar) and understand how LLM prompts/tools/agents fit together. Think in cause-and-effect: “the agent did X because line Y / instruction Z.” Write precisely and concisely for both technical and non-technical audiences. Are process-minded — you spot the repetitive thing and turn it into a better workflow. Bonus: prompt engineering, LLM tool/agent development, or voice/conversational AI experience. How we work We’ll start with a paid trial on a small batch, then scale steady ongoing volume. To apply: Tell us about a time you used an AI coding agent to diagnose or fix something non-trivial in a codebase you didn’t write — what you did, and how you verified it worked. A link to relevant work is a plus.

We are hiring an AI Engineer with strong hands-on experience building and shipping real AI products. Requirement: If you don't have a GitHub profile to share, this role is not a fit. What we’re looking for: • Strong experience in AI/ML engineering • Ability to build, test, and deploy production-ready AI systems • Practical experience working on real-world AI projects To apply: Please share your portfolio, past AI projects, and relevant work samples. Applicants without portfolio will be ignored.

Developer Scope of Work Project Overview & Engagement Terms Domexa Labs for MyCondoCompliance (mycondocompliance.com). MyCondoCompliance is an enterprise and consumer-facing web platform built to aggregate, OCR, analyze, and report on condominium association compliance data throughout Florida (starting with Miami-Dade County). 1. Key Engagement Expectations: Dedicated Weekly Support: We require reliable, continuous development capacity week-over-week to support platform growth, new features, maintenance, and internal system updates. Flexible Monthly Hours: Hours will flex on a month-to-month basis depending on business priorities, product release cycles, and current backlogs. Minimum 2-3 hours/week, not to exceed 15hrs/week. Rapid Turnaround & Steady Communication: We operate in a fast-paced environment. Quick turnarounds on hotfixes, active updates on tasks, and daily/structured communication are critical. Language Requirement: Excellent, professional verbal and written English is a strict requirement for technical syncs, documentation, and coordination. 2. Technical Infrastructure & DevOps Architecture The MVP is complete, live, and deployed. You will inherit the following technical ecosystem: Infrastructure Stack: - Code Repository: Managed via GitHub. - Front-end Hosting: Deployed and managed on Netlify. - CI/CD: Automatically triggers deployment to production on master branch updates, and to staging/dev on dev branch updates. - Back-end Hosting & Infrastructure: Managed on Digital Ocean inside a Kubernetes environment. - DNS Administration: Managed on Digital Ocean. - Third-Party API Integrations: - Mapbox: Powers map-based search and property discovery. - Mailgun: Handles transactional email delivery. - Chatbase: Integrated for natural language querying and chat functionality. - TipTap: Rich text editor powering board notes and internal editing. 3. Scope Evolution & Core Pipelines As our incoming developer, you will be expected to maintain, debug, and expand upon the core features built during our initial execution phases. A. Data Pipelines & OCR Ingestion Engine - Website Scraper/ETL: Continuous ingestion pipelines that pull structured condo data and metadata from county public registers. - Normalization Engine: Ingestion pipeline that categorizes incoming unstructured documents into strict schemas - OCR & Vectorization: All ingested documents are automatically processed via an OCR layer, and the resulting plaintext is indexed into a vector database for semantic search and Retrieval-Augmented Generation (RAG). B. Autonomous AI Processing Agents We run specialized Python/Node microservices to process aggregated document metadata: - Granular Extraction: AI agents systematically query vector databases to extract critical datapoints - Audit Trails & Provenance: Each extracted datapoint must carry verification properties—linking back directly to the document source, specific page/snippet, and extraction timestamp. C. Portal Tiering & Client Features - Consumer Interface: Detailed property pages, dynamic scoring components, PDF report compilation and downloads. - Enterprise Interface: Multi-tenant web app allowing real estate, financial, and legal clients to access deep search, structured list filtering (e.g., filtering condos by unit counts, reserve posture, specific clauses such as "Kauffman language", and termination criteria), and batch export controls. - Admin Dashboard: Tracks user engagement metrics, domain lookups, purchase histories, and mailing list extractions.

Need an experienced developer to integrate an AI-powered feature into an existing application. Small scope, fast turnaround