Building an AI-powered trading intelligence platform backed by one of the biggest stock-trading YouTubers in the space (7-figure audience, direct distribution to our exact target users from day one). We're not looking for product-market fit — we have a warm audience waiting. We're in the final push to V1 launch and shipping fast. What we've already built An AI Strategy Builder: traders describe a strategy in plain English, our LangGraph agent pipeline (Claude) turns it into code, and a NautilusTrader engine backtests it against years of tick-accurate market data A real data moat: TimescaleDB with 10 years of futures/equities data Live market intelligence: screeners, regime classification, probability models What you'll build You'd own big, meaty features end to end - not tickets, not maintenance: The strategy optimization engine: run 60+ market signals as filters over backtest results, rank by statistical impact, present improvements to users (this is our flagship differentiator) Statistical validation: walk-forward testing, overfitting protection Market calculators: VWAP, volume profile, pre-market levels A daily AI trading playbook delivered via text/Discord Real-time pipeline health monitoring Our stack Python/FastAPI · LangGraph + Claude (Anthropic API) · NautilusTrader · TimescaleDB/Postgres · React/TypeScript/Vite · NestJS · WebSockets · AWS Who we need Someone cracked. Specifically: 5+ years shipping production Python backends (FastAPI/Django/Flask) — you write code that survives contact with real users Real LLM engineering experience — agent pipelines, structured outputs, prompt-driven codegen, LangGraph/LangChain (or you've built equivalents from scratch) Strong SQL and data chops — you're comfortable with time-series data at scale Full-stack ability — you can carry a feature from Postgres to React without waiting on anyone Trading/quant/fintech domain experience is a big plus — you know what Sharpe, drawdown, and walk-forward mean without Googling You ship daily, communicate crisply in Slack, and don't need a PM to translate ambiguity into work Why this gig Direct line to founders, zero bureaucracy — your code hits production the week you write it Guaranteed distribution at launch via our backer's audience — your work gets used by thousands of real traders immediately Long-term engagement for the right person, with room to grow into a lead role

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 want to create Dream Life Visualization Videos using AI. If you search 'billionaire visualization' videos on yt, this is sort of the style i'm going for but not exactly the same. -Obviously, these videos will be created using realistic AI clips (the longer the clip the better; 20 second clips) -all visuals will be from a first-person point of view -I want sound design on the video to bring the scenes to life -I want to implement consistent characters This first job is a test Before hiring anyone I will need to see previous work (realistic AI video creations) If we have good communication, understanding and you create satisfactory visuals I am looking to work consistently together. I will provide reference images along with descriptions of what I want to be happening in the clip for every clip that will be generated by you. Your job is to create a precise prompt that will create the image scene i'm asking for. YOU MUST BE A PROMPT ENGINEER MASTER, not an amateur that writes prompts and hopes it comes out good. My goal is for each full video to be around 5 minutes long & every clip should is 15-20 seconds long to really give the viewer time to soak in the experience of the scene (that is the point of visualization). This means... (20 sec clips = 15 clips generated) -I want to add dissolve/fade transitions between each clip. -I want every clip to be from the 1st person pov. -Realism is priority.

US-BASED CONTRACT FULL-STACK / AI ENGINEER FOR HEALTHCARE PRODUCT Remote, US-Based Contractor | East Coast Time Required | Home Health AI Product About Scribble Scribble is the top-rated AI platform for home health purpose-built to give clinicians their time back. Home health agencies run on documentation: visit notes, care plans, prior authorizations, compliance paperwork - we help automate these processes. Project Overview We are looking for one or more experienced US-based contractors to help us move faster across several important areas of our product. We need hands-on builders who can scope clearly defined product needs, ask the right questions, and independently deliver high-quality work. This is not a narrow ticket-taking role. We are looking for people who can own meaningful pieces of the product, communicate clearly, work asynchronously, and make steady progress without heavy day-to-day management. Depending on your strengths, the work may focus on mobile, backend, AI workflows, and EMR robotic process automation. You will ideally own end to end development including testing. What We Need Help With • Build and improve visit types and clinical documentation workflows for home health use cases • Improve accuracy of AI-generated outputs, including prompt design, evaluation, testing, and workflow refinements • Integrate completed visits and documentation into EMR systems using APIs where available and robotic process automation where needed • Work across React Native mobile app features, Node.js backend services, OpenAI/LLM workflows, and automated testing • Debug production issues, improve reliability, and help us ship quickly without sacrificing quality • Troubleshoot and fix bugs quickly, and make product improvements based on customer feedback • Translate product requirements into practical technical plans and independently execute against them • Document decisions, provide frequent updates, and proactively flag risks, blockers, and trade-offs • Potentially collaborate with team members across different parts of the roadmap Skills We Are Looking For Required • Advanced use of Claude Code • US-based contractor with East Coast time zone availability required • Strong experience with React Native mobile development • Backend experience with Node.js, APIs, databases, authentication, and production debugging • Hands-on experience with OpenAI or other LLM APIs, prompt engineering, structured outputs, and AI workflow testing • Strong testing mindset, including unit tests, integration tests, regression testing, and quality checks for AI outputs • Ability to work independently from a product goal, break it into technical tasks, and deliver without constant direction • Excellent written and verbal communication; concise updates, clear questions, and proactive status reporting are essential • Speed and quality are both must-haves: we need someone who can move quickly while still shipping reliable, well-tested work • Comfortable working with sensitive healthcare data and following HIPAA-aware, security-conscious development practices Strongly Preferred • Meaningful healthcare experience is strongly preferred, especially in home health, clinical documentation, EMR/EHR workflows, HIPAA-aware development, or regulated healthcare environments Contract Details • Contract role for a US-based independent contractor • US-based candidates only; East Coast time zone availability is required • Part-time or project-based to start, with potential for ongoing work • Minimum availability of 20 hours per week is required • We may hire multiple contractors based on specialty, fit, and availability • Clear deliverables, frequent communication, and fast iteration cycles • Hourly rate or fixed-price milestones can be discussed based on scope and experience • Selected contractors will need to sign appropriate IP assignment, NDA, and Business Associate Agreement documents before accessing sensitive product or healthcare data • We may request and check references before starting a larger engagement

AI Automation Engineer – Personal Injury Law Firm (Phase 1) Overview We are a plaintiff personal injury law firm seeking an experienced AI automation engineer to build a practical AI-powered operations system that reduces administrative workload and improves case management. Firm Profile: - 1 Attorney - 1 Legal Assistant - Approximately 100 Active Cases - Approximately 25 Cases in Litigation We are not looking for chatbots, prompt engineering, or marketing automation. We are seeking a builder who can deploy production-grade workflow automation integrated with our existing systems. --- Existing Technology Stack - CASEpeer - Supio - Gmail (Google Workspace) - Google Calendar - Google Drive - Claude - ChatGPT - CoCounsel - PLAUD --- Phase 1 Objective Build a Daily Case Command Center and Follow-Up System. The goal is to ensure that every morning the attorney knows exactly which files require attention and why. --- Deliverable #1: Daily Case Command Brief Generate a daily briefing containing: Litigation Matters - Upcoming depositions - Discovery deadlines - Hearings - Expert deadlines - Outstanding litigation tasks Pre-Litigation Matters - Treatment complete but no demand - Missing medical records - Records requests outstanding more than 21 days - Demands pending more than 30 days - Settlement checks outstanding Communications - Unanswered client emails - Unanswered adjuster emails - Unanswered defense counsel emails - Communications requiring attorney attention Case Velocity - Stale files - Cases with no recent activity - Recommended next actions --- Deliverable #2: Gmail Intelligence The system should: - Monitor designated Gmail inboxes and labels - Identify case-related emails - Extract action items - Detect deadlines - Identify follow-up opportunities - Draft suggested responses --- Deliverable #3: Follow-Up Automation Generate draft communications for approval: - Medical records requests - Provider follow-ups - Adjuster follow-ups - Client status updates - Scheduling communications No communication should be sent automatically. Human approval is required before sending. --- Deliverable #4: Calendar & Reminder Automation Create and manage: - Follow-up reminders - Litigation reminders - Discovery reminders - Records request reminders - Suggested calendar events --- Deliverable #5: CASEpeer Integration Where supported by available integrations/APIs: - Create tasks - Create notes - Associate communications with matters - Maintain activity history --- Technical Requirements Required: - n8n - Python - OpenAI API - Anthropic API - Gmail API - Google Calendar API - Google Drive API - REST API integrations Strongly Preferred: - LangGraph - LangSmith - Google Workspace administration - Salesforce integrations - CASEpeer, Clio, or Filevine experience - Legal technology experience - HIPAA or regulated-data experience --- Security Requirements - Human approval before external communication - Activity logging - Error handling - Retry mechanisms - Secure credential management - Documentation of workflows --- What Success Looks Like The system should: - Reduce administrative workload - Improve follow-up consistency - Reduce stale files - Improve litigation oversight - Improve case visibility - Provide a daily prioritized action plan --- Application Requirements Please include: 1. Similar workflow automation projects you have completed. 2. Experience with n8n and AI workflow automation. 3. Experience integrating Gmail and Google Workspace. 4. Experience integrating CRM or case-management systems. 5. Proposed architecture for this project. 6. Estimated timeline. 7. Estimated fixed-fee budget. Begin your application with: CASECOMMAND Applications without this keyword will not be considered. --- Budget Expected Phase 1 Budget: $8,000–12,000 Preference will be given to candidates who can demonstrate production deployments rather than proof-of-concept or prompt-engineering projects.

We're building a confidential, AI-native operating system for high-volume plaintiff-side litigation. This is not a generic legal chatbot. It's an operating system for litigation operations — and we already have a live law firm as the proving ground, a working visual prototype, a pitch deck, and a near-term demo deadline. We need a senior full-stack engineer who can take an existing prototype, schemas, prompts, and workflow package and turn it into a secure working demo, then a production-track MVP. The right person thinks like a product architect, engineer, and security operator at once — fast, but disciplined with confidential legal data. Required: React/Next.js, TypeScript, Node or Python/FastAPI, PostgreSQL, auth and role-based access control, OpenAI or comparable LLM APIs, structured JSON/schema outputs, secure file handling, PDF/export generation, GitHub workflows, and strong security discipline. Strong plus: Legaltech, plaintiff-side litigation, case management systems (Filevine, Litify, Clio, Salesforce, HighLevel), RAG/document extraction, audit logging, and SOC 2 / PII / regulated-data experience. Ground rules: NDA required. No public repos. No real client data in the demo — sanitized data only. No API keys in browser code. No external sharing or deployment without approval. First deliverable: A build-readiness report identifying what's mock, what's reusable, and what needs rebuilding, plus architecture, security risks, database plan, API integration path, and a 7–30 day build roadmap. The path: Paid 7-day build-readiness sprint → 30-day demo sprint → longer-term technical lead / founding engineer discussion. To apply, please include: A short note on why you're right for this project 2–3 relevant products you've built (links) GitHub or code samples, if available Your availability for a 7-day build-readiness sprint Your hourly rate, fixed sprint price, or contract-to-hire preference Remote acceptable. U.S.-based preferred; South Florida a plus.

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.

Senior React Native Engineer – AI Integration Experience Required Overview We are looking for a Senior React Native Engineer with production AI integration experience to build an intelligent AI assistant into our existing iOS and Android application. Our application is already live. The user experience and AI chat interface have already been designed. Your responsibility is to build the AI functionality and integrate it seamlessly into our existing React Native application and Laravel backend. This is not a basic chatbot project. We are looking for someone with real experience building production AI applications using the OpenAI API, function/tool calling, and API integrations. Existing Technology Stack React Native Laravel MySQL Firebase AWS iOS Android Project Overview Users should be able to ask questions naturally, such as: "I'm struggling today." "Explain the Fifth Tradition." "Find a meeting tonight." "Find long-term sober living near me." "Find treatment within 20 miles." The assistant should intelligently determine how to answer each request. Sometimes it should respond directly. Sometimes it should search our internal database. Sometimes it should call external APIs. Sometimes it should combine multiple data sources into one response. Core Responsibilities Build and integrate: AI chat functionality OpenAI API integration Function / Tool Calling Conversation history Laravel AI backend Internal database search External API integrations Structured recommendation cards Production-ready implementation Resource Search When users request nearby resources, the AI should return structured recommendation cards containing: Facility Name Address Phone Number Website Each card should include: Call Directions Website APIs & Integrations Experience with the following is preferred: OpenAI API Function / Tool Calling REST APIs Laravel APIs Google Places API (or equivalent) Public API integrations Required Experience Senior React Native OpenAI API AI integrations Laravel REST APIs Production mobile applications Preferred Experience AI memory Prompt engineering Semantic search RAG Vector databases Deliverables The completed MVP should include: AI chat integration Backend AI service OpenAI integration Internal database search External API integrations Structured recommendation cards Conversation history Production-ready implementation Please Answer Have you built production AI applications using the OpenAI API? Please provide links to AI applications you personally built. Have you implemented OpenAI Function / Tool Calling? Have you integrated AI with existing databases? Describe the architecture you would recommend for this project. Walk us through exactly how your system would process the following request: "Find me a long-term sober living facility within 20 miles." Estimated timeline. Please provide a milestone-based fixed-price estimate. Important We are looking for someone who has built production AI applications, not someone who has only experimented with AI. A video interview is required before hiring. During the interview, you'll be expected to discuss your previous AI projects, explain your proposed architecture for this project, and answer technical questions about your implementation approach. Only applicants willing to participate in a video interview will be considered. This project has the potential to become a long-term relationship as we continue expanding our AI platform. Please begin your proposal with the word Recovery so we know you read the entire posting. Budget will depend on your proposed architecture, experience, and scope. We are looking for the best long-term solution—not necessarily the lowest bid.

I’m running a real estate investment platform called ToInvested.com. The project is about 90% finished, and most of the code was built with Claude together with another engineer. Now I need a senior engineer to step in, review the full product carefully, test every major workflow, and help verify that everything is working correctly before it goes live. This is not just a “write more code” role. I need someone who can look at the platform like a real product, find hidden bugs, catch weak logic, test edge cases, review the AI-generated code, and tell me honestly what is ready and what still needs fixing. Because this is a real estate investment platform, accuracy and trust matter a lot. Users may rely on property data, investment logic, calculations, and AI-driven insights, so even small issues can create a serious problem later. The ideal person has strong full-stack experience, understands AI-assisted development, and has a good testing mindset. Real estate tech experience would be a big plus, especially with property platforms, investment tools, marketplaces, mortgage systems, or financial workflows. My main goal is simple: I want someone to break the project before real users do. If you’re the kind of engineer who can take a nearly finished product, test it deeply, clean up weak areas, and help make it production-ready, I’d be happy to talk.

DESCRIPTION We're a small applied AI lab running a live, production-track AI product for an institutional financial services client. The work is technical, fast-moving, and high-stakes. We need to fill a critical infrastructure role with someone senior, collaborative, and genuinely excited about building in the current AI tooling ecosystem. THE ROLE You'll own the data infrastructure layer for an AI-powered intelligence platform built on the Microsoft Azure ecosystem. This is a hands-on engineering position — you're responsible for designing, building, and maintaining the pipelines that feed a live AI scoring engine. The environment is agentic. Data moves from 15+ heterogeneous external sources (APIs, PDFs, regulatory filings, web) through Bronze, Silver, and Gold layers into a scoring and inference system. The hard problems are extraction quality, schema normalization, pipeline reliability, and getting the right data to the scoring engine in the right shape. You'll work directly with the technical lead and engagement lead. No layers. Fast decisions. WHAT YOU'LL OWN + Data pipeline architecture and delivery across Bronze (raw ingestion), Silver (normalization, NLP extraction, entity resolution), and Gold (unified output, scoring-ready) layers + Microsoft Fabric lakehouse implementation — OneLake, Data Pipelines, Dataflows Gen2, Warehouse, and downstream system integration + Microsoft Foundry (formerly Azure AI Studio) — agent orchestration, prompt pipelines, and AI model integration within a secure Azure tenancy + Azure Data Factory orchestration for structured source ingestion +Salesforce integration via Snowflake native connector — field mapping, custom object schemas, sync reliability Extraction pipelines for unstructured sources (PDFs, regulatory filings, web content), coordinating with Azure OpenAI-based extraction agents +Data governance and security posture — all data stays within the client's Azure tenancy; data residency is non-negotiable REQUIRED: Technical Skills + Microsoft Fabric — production experience, not sandbox. You should be able to speak to Lakehouse vs. Warehouse tradeoffs, OneLake architecture, and real pipeline implementation. Microsoft Foundry / Azure AI Studio — hands-on with agent deployments, prompt flow, model endpoints, and Azure OpenAI integration within an enterprise Azure tenancy + Azure Data Factory — pipeline authoring, trigger management, connector configuration, monitoring +Snowflake — Gold layer data warehousing, schema design, query optimization, native connector usage (specifically Salesforce) + Python — data engineering contexts: pandas, PySpark, API clients, extraction scripts + SQL — complex joins, window functions, schema design; SQL Server preferred + Azure Blob Storage / ADLS Gen2 — Parquet/Delta format, access control, lifecycle management REQUIRED: AI-Augmented Development This is a hard requirement. You should be actively using AI coding tools to multiply your output — fluency with Claude Code, Cursor, and OpenAI Codex as part of your daily development workflow. If these aren't already in your stack, this isn't the right fit. We hire for multiplied output, not raw hours. REQUIRED: Demonstrable Work We don't evaluate resumes alone. Bring something — a GitHub repo, a deployed pipeline, an architecture document you authored, a case study with real numbers. We should be able to look at your work and understand what you built, what decisions you made, and why. Work under NDA is fine if you can describe it in enough detail to convey complexity and ownership. ATTITUDE & WORK STYLE Comfortable with Agile Scrum and its accompanying ceremonies. You raise issues early and help solve them. You communicate tradeoffs clearly without over-explaining. You're comfortable with evolving specs and don't need to win the architecture argument — just build the right thing within the approved stack. We're a small, senior team with low friction and direct communication. That's the environment; it works if you work with it. THE STACK The client environment has specific technology approvals. Production work runs on Azure OpenAI (client-hosted), Microsoft Fabric, Microsoft Foundry, Snowflake, Azure Data Factory, ADLS Gen2, Salesforce via Snowflake native connector, and SQL Server. LangChain, DeepSeek, and the external Claude API are not approved for this environment. NICE TO HAVES Experience with financial services or institutional investment data (SEC EDGAR, public pension filings, regulatory documents), familiarity with InvestorFlow or Salesforce Financial Services Cloud, unstructured document extraction at scale, or Azure Purview.