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

AI Engineer (Agentic AI, RAG & Workflow Intelligence) We have already launched an initial generative AI product that uses RAG architecture to generate response. The RAG retrieves data from SQL and data from documents on MongoDb vector search. We are now seeking an experienced AI Engineer to help improve our current AI architecture to make it more dynamic and also implement agentic AI capabilities across a new CRM feature that we are building. You will be working with other full stack devs who will be building the memory and storage which is vital to our intended AI integrations, but you need to guide on best practices. We are looking for someone who has previously built production AI systems (generative and agentic), comfortable walking into existing complex codebases, and is excited about designing systems that become indispensable to actual users. Most importantly, has HIGH ACCOUNTABILITY and who believes that understanding users, workflows, and product vision comes before writing code. AI Architecture & System Design Audit, understand, and improve our existing AI architecture. Improve retrieval quality, response quality, memory systems, latency, and overall AI performance. Design scalable long-term memory systems across emails, texts, whatspp, video call transcripts communications, documents, tasks, and other user records. Build proactive AI agents that generate alerts, recommendations, follow-ups, reports, and next actions. Design systems that combine structured and unstructured data into highly contextual user experiences. Architect and implement multi-agent workflows per instructions Intelligence & Workflow Systems Build systems capable of reasoning across the following user records/data: CRM records Emails and communications Calendar activity Documents and contracts Notes and tasks Call transcripts and meeting summaries Workflow history Required Experience Experience building and deploying AI products used by real customers with verifiable references. Proven experience building production RAG systems. Experience programming AI agents and workflow automation systems. Demonstrated ability to quickly understand and improve existing codebases. Strong software engineering and systems design fundamentals. Ability to work independently and take ownership of complex technical initiatives. Preferred technologies include: Python TypeScript / JavaScript Node.js FastAPI MongoDB compass and atlas & PostgreSQL OpenAI, Anthropic, Grok APIs Docker and cloud infrastructure To Apply: All AI responses will be ignored. Interview will be video call (camera on). We expect you to be able to explain in plain english your experience and work product.

I’m looking for an AI Engineer to help build an automated red-teaming product based on open-source models. This is a short-term, hands-on project for around 2 months, with an expected commitment of about 20 hours per week. The goal is to build a specialized red-teaming engine that can generate adversarial prompts across different risk domains, severity levels, and attack strategies — then automatically run those prompts against target AI models to identify bad cases, failure patterns, and safety gaps. 🔍 What you’ll work on Build red-teaming systems on top of open-source LLMs, including fine-tuning, prompt optimization, evaluation pipelines, and model orchestration. Design automated prompt generation workflows across risk domains such as self-harm, hate, violence, sexual safety, misinformation, fraud, cyber, and other high-risk areas. Generate prompts across different harm levels, from benign edge cases to policy-borderline and clearly unsafe scenarios, while maintaining structured taxonomies and evaluation criteria. Run automated tests against target models such as Gemma, Llama, Qwen, or other open-source / closed-source models to surface jailbreak patterns, over-refusal, under-refusal, and policy inconsistencies. Build feedback loops that turn model failures into stronger red-team prompts, improved eval sets, remediation recommendations, and continuous safety testing. 🧠 What I’m looking for Hands-on experience with open-source LLMs, fine-tuning, LoRA / QLoRA, RAG, model evaluation, and LLM inference pipelines. Familiarity with AI safety, red teaming, adversarial prompting, jailbreaks, safety evals, or trust & safety systems. Ability to build end-to-end systems, including data pipelines, model serving, eval harnesses, scoring, dashboards, and automation workflows. Bonus if you’ve worked on model safety, content moderation, policy evaluation, agentic testing, or automated eval infrastructure. ⏳ Project setup Duration: around 2 months Time commitment: about 20 hours per week Format: flexible / remote-friendly Stage: early-stage build, from 0 to 1 🚀 Why this is interesting This is not about manually writing red-team prompts one by one. The goal is to build a scalable system that can continuously generate, test, categorize, and learn from model failures — helping teams understand where AI models break, why they break, and how to improve them. If you enjoy working with open-source models, AI safety, red teaming, and fast 0-to-1 product building, I’d love to chat. Feel free to DM me if this sounds like you, or if you know someone who might be a good fit.

We're looking for an AI & Data Analysis expert to lead the integration of intelligent tools within the business platform. You'll connect Google Ads, marketing data files, and operational data sources to build AI agents via Claude that support business decision-making across our pet retail operations. Key Responsibilities Design and configure Claude-powered agents using tool use, structured prompts, and automated workflows for data analysis Integrate the Google Ads API to extract campaign metrics and feed decision-making dashboards Ingest, clean, and structure CSV, Excel, and other marketing data formats for agent processing Generate automated narrative reports and actionable visualizations for the executive and marketing teams Maintain and iterate on data pipelines connecting advertising, sales, and inventory data Required Technical Skills Claude API / Anthropic MCP (Model Context Protocol) Prompt engineering and LLM tool use / function calling Google Ads API Python or JavaScript (for pipelines and integrations) SQL / PostgreSQL / Supabase Pandas / NumPy or equivalent data libraries REST API consumption and integration Advanced Excel / Google Sheets Nice to have: Google Analytics, BigQuery, Looker, Power BI Ideal Profile Proven experience building data pipelines or LLM-powered tools in a production environment Hands-on familiarity with the Anthropic API and agent/tool-use patterns Ability to translate raw data into clear, actionable business recommendations Self-directed — can propose and build solutions without exhaustive specs Initial Projects Campaign ROI Agent — connects Google Ads + business sales data to generate automatic performance alerts and recommendations Marketing File Pipeline — ingests CSV/XLSX marketing files and produces AI-generated summaries and insights Executive Dashboard — decision-support interface with Claude-generated action recommendations based on live data

AI Developer Needed – Build Us a Marketing AI Agent We need a skilled developer to build an AI-powered Marketing Assistant for our business. **Core Tasks the Agent Will Handle:** - Appointment setting & lead qualification - Copywriting (emails, ads, social content) - Automated follow-up sequences - Lead research and CRM updates **Requirements:** - Experience with AI agent frameworks (LangChain, CrewAI, AutoGen, etc.) - Strong prompt engineering skills - Ability to integrate with our existing tools (CRM, calendar, email) - Past projects to show us – links or demos preferred **Budget:** Open to discussion based on scope **Timeline:** Looking to kick off within 1–2 weeks

Overview We’re looking for an experienced AI engineer or AI systems builder to help us design and build an internal intelligence layer that turns fragmented customer data into actionable growth opportunities. Right now, customer insights live across multiple disconnected systems — CRM notes, product usage data, emails, support tickets, and spreadsheets. While the data exists, it is not structured in a way that helps us proactively identify expansion opportunities, churn risks, or account-level next steps. We want to build an AI-driven system that continuously synthesizes this information and helps our team understand: * What is happening inside each account * Where expansion or upsell opportunities exist * Which accounts are at risk and why * What the next best action should be for each customer ⸻ What You’ll Build You will design and implement an AI system that can: * Ingest structured and unstructured data (CRM, emails, notes, product signals) * Build dynamic “account intelligence profiles” for each customer * Identify patterns across accounts (usage drops, feature gaps, expansion signals) * Generate clear, human-readable account summaries * Recommend next-best-actions for sales, customer success, or leadership * Surface expansion opportunities based on behavioral and contextual signals * Flag risk signals early with supporting reasoning ⸻ Ideal Output For each account, the system should be able to generate: * A concise account narrative (“what’s going on here”) * Key signals and anomalies * Expansion opportunities (with rationale) * Risk factors (churn or stagnation indicators) * Suggested actions for the team this week * Confidence level and supporting evidence ⸻ Why This Matters We are sitting on a large amount of customer data, but most of it is passive. The goal is to turn it into an active intelligence system that helps our team: * Prioritize the right accounts * Increase expansion revenue * Reduce churn risk * Spend time on the highest-impact opportunities This becomes a core internal system that directly impacts revenue efficiency and customer outcomes. ⸻ Ideal Candidate We’re looking for someone with experience in: * LLM-based systems and agentic workflows * Data pipelines and multi-source data ingestion * Prompt engineering + structured reasoning systems * CRM systems (Salesforce, HubSpot, etc.) * Customer analytics / product analytics * Building internal AI tools or copilots * Backend + API integration work Bonus if you’ve worked on: * RevOps tooling * Customer success platforms * Data enrichment or account intelligence systems * SaaS growth analytics ⸻ Deliverables * System architecture for AI customer intelligence layer * Data ingestion and normalization approach * Prompting / reasoning framework for account analysis * Prototype system (or working MVP) * Output format for account intelligence reports * Documentation for internal expansion and scaling * Recommendations for tooling (build vs buy decisions) ⸻ Engagement This starts as a project-based build, but could expand into a long-term role as we scale the system across our entire customer base and additional workflows. ⸻ To Apply Please include: * Examples of AI systems or agentic workflows you’ve built * Experience integrating LLMs with real business data * Your recommended architecture for a system like this * Any clarifying questions you’d want answered before starting

Forum Intelligence: Project Brief & Initial Rollout 1. Executive Summary & Objective Forum Intelligence is a beginning as a localized data retrieval, processing, and archiving system designed to scrape public municipal records and state legislative data for public oversight. The immediate objective is to build a functional, highly resilient prototype focused on the Tri-Cities region (Burbank, Glendale, and Pasadena, California). The system will autonomously ingest messy, unstructured municipal data (City Council meeting minutes, agendas, public notices, and legislative PDF text, recorded mp4), clean it, and make it fully searchable and queryable via a localized AI agentic framework. 2. Phase 1 Scope: The Tri-Cities Rollout Th engineer will be responsible for building two primary pillars: A. Resilient Scraper Bots • Target Ingestion: Monitor and pull data from Burbank, Glendale, and Pasadena municipal portals and California legislative feeds. • Data Types: Brittle HTML sites, heavily nested tables, public notices, legislative drafts, and massive unstructured PDF archives. • Requirements: The scraping architecture must be exceptionally robust, utilizing intelligent error handling, retry semantics, and pagination tracking to handle frequent municipal website layout changes without breaking the pipeline. B. Ingestion & Vector Pipeline • Parsing: Extracting clean text from poorly formatted documents and scanned PDFs. • Local RAG (Retrieval-Augmented Generation): Chunking and embedding the data locally into a vector database (e.g., pgvector, Chroma, or Milvus) to enable semantically accurate entity linking and contextual search. 3. Targeted Hardware Stack To ensure maximum data security, strict public oversight integrity, and predictable operational costs, Forum Intelligence is skipping commercial cloud APIs in favor of an on-premise, localized NVIDIA enterprise deployment. The production roadmap aligns precisely with the new computing patterns detailed in NVIDIA’s latest hardware roadmap: • Inference & Token Generation: Running local open-weight frontier models (e.g., Neotron 3 Ultra or Claude/Llama equivalents) optimized for reasoning and long-context tool use. • Compute & Orchestration: The backend infrastructure is architected around NVIDIA’s dedicated agentic architecture, utilizing high-instructions-per-clock (IPC) Vera CPUs paired with Vera Rubin GPUs. • Memory & Storage Processing: Utilizing NVIDIA’s unified memory fabric and data processing units (DPUs) for ultra-low latency context management, KV caching, and fast vector database retrieval. 4. Immediate Milestones for the Engineer 1. Architecture Design: Map out the database schema and local inference ingestion loop. 2. Tri-Cities Scraper Deployment: Write and deploy the initial automated bots for Burbank, Glendale, and Pasadena. 3. Local MVP Pipeline: Demonstrate a local RAG pipeline where a user can query the Tri-Cities scraped records and receive grounded answers with exact source attributions. The above was AI generated from months long conversations with Gemini. The goal is to prove the concept then roll out to LA County, state of CA, and then the country.

I am building Dewy, an early-stage construction technology platform focused on construction buyout and subcontractor quote intelligence. The first MVP is intentionally narrow: users should be able to upload subcontractor quote/proposal documents and receive structured outputs showing included scope, exclusions, assumptions, qualifications, cost structure, alternates, allowances, and potential risk flags. I have already developed the product concept, construction logic, early workflows, and prototype direction using Codex/AI tools. I am not looking for someone to invent the product from scratch. I need a senior AI product engineer who can review what I have, determine what is usable, define a clean MVP architecture, and help turn the current direction into a working private beta. Initial scope: * Review the current prototype/code/product materials. * Identify what should be reused vs. rebuilt. * Recommend the MVP architecture and tech stack. * Define the AI document-processing workflow. * Design the structure for file upload, extraction, editable results, and export. * Help create a realistic build roadmap, timeline, and budget. * Potentially continue into hands-on MVP development if there is a strong fit. Ideal experience: * Full-stack SaaS / MVP development * AI / LLM application development * OpenAI API or similar model integrations * Document extraction or document intelligence workflows * PDF/DOCX parsing and structured data extraction * React / Next.js * Python * APIs and backend workflows * Supabase/Postgres or similar database experience * Vercel or similar deployment experience * Ability to work with a non-technical founder and translate business goals into a practical build plan This is not a full enterprise platform build yet. The first MVP should stay focused on one core workflow: Subcontractor quote documents in → structured buyout intelligence out. Please respond with: 1. Relevant AI/document extraction projects you have built. 2. How you would approach the MVP architecture. 3. Whether you recommend starting with an audit/roadmap before build. 4. Your hourly rate and availability. 5. Whether you are interested in ongoing build involvement after the initial review.

We’re hiring a Senior Full-Stack AI Engineer to build an MVP for an LLM-powered recommendation platform. The role involves developing questionnaires and structured AI output to enhance user experience. The ideal candidate will have experience in AI and full-stack development, with a focus on delivering efficient and effective solutions.

I'm seeking an expert to develop an AI agent that will assist in managing my business. The AI should be able to handle tasks such as scheduling, data analysis, and customer communication. The ideal candidate will have experience in business management and AI development, and be able to deliver a functional prototype within a month.