We are seeking a skilled software developer to finialize our Software development and create a multi-agent orchestration platform. The role involves fininalizing/creating our software development of out Ai-Native TMS, creating a multi-agent orchestration platform that creates all necessary agents to improve system reliability, and optimizing performance. The ideal candidate will have experience in software development and a strong understanding of multi-agent systems. You will work closely with our team to ensure seamless integration and deployment of new features.

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

We are seeking a US-based computer vision and full stack developer to build a platform for sports card recognition. The project includes developing subscription management, dashboards, and user account features. The ideal candidate will have experience creating scalable applications and integrating computer vision capabilities into a user-friendly platform. Hiring: Computer Vision + Full Stack Developer for Sports Card Live Auction Overlay App (SaaS) 📌 Overview I’ve built an MVP of a real-time sports trading card scanning and comping overlay tool using Loveable.dev. The product helps buyers gain an edge during live auctions by instantly identifying cards and showing real-time market comps. Now I’m looking for a U.S.-based developer (or strong US-aligned freelancer) to take this from MVP → production SaaS. This is a subscription-based product, so I need someone who can help build something fast, accurate, scalable, and hard to replicate. 🧠 What the product does Users can: Capture or upload sports trading card images during live auctions (mobile + desktop) Instantly identify: Player Year / set Parallel / serial number Pull live market comps Display a real-time “buy / avoid / fair price” overlay The goal is speed + accuracy in live buying situations (seconds matter). ⚙️ What I already have MVP built in Loveable.dev Basic overlay + UI flow Initial comp logic concept Subscription idea (not yet fully implemented) 🛠️ What I need help building (Phase 1 → Scale) I’m looking for someone to help rebuild and harden the system into a real SaaS product: 1. Computer Vision / OCR Layer Card detection from images (mobile + desktop) OCR extraction (player name, set, serial numbers) Image recognition / matching to known cards Confidence scoring (very important — must avoid wrong matches) 2. Comp Engine (Core Value) Integrate or build system for: eBay sold listings 130point or similar comp sources Card Ladder / ALT-style pricing logic Return: last sale average comp trend direction liquidity estimate 3. Real-Time Overlay System Lightweight overlay that works during live auctions Low latency (fast lookup is critical) Works on mobile + desktop workflows 4. SaaS Infrastructure User accounts + authentication Subscription billing (Stripe) Usage tracking / rate limiting Admin dashboard 5. Scaling / Production Hardening API architecture improvements Database structure Performance optimization for real-time use Error handling for imperfect images 💡 Ideal candidate You should have experience with: Computer vision (OpenCV, YOLO, or similar) OCR pipelines AI image classification or similarity matching Full-stack SaaS development Stripe subscriptions API design (Node.js / Python / Next.js preferred) Huge plus if you have: Sports card / collectibles knowledge Experience with marketplaces or scraping pricing data Real-time / low-latency systems 🎯 Why this is interesting This is not a generic app. It’s: A real-time decision engine for high-value collectibles Built for a passionate, high-spend niche (sports cards) Subscription-based with strong monetization potential Designed for speed advantage in live auctions 📍 Requirements Must be U.S.-based (preferred for communication/time zone alignment) Must be able to work independently Must have strong GitHub/code examples Bonus if you’ve built AI or vision-based SaaS tools before 💰 Budget Open to: Hourly or fixed project 📩 To apply, please include: Relevant CV / GitHub Past AI / computer vision projects Any SaaS or startup experience Your approach to building a real-time image → comp system Availability per week

Existing founder is looking for a full stack engineer to be the founding engineer at Socratix, an agentic AI powered data platform for sales teams connecting Zoom, Teams and other communication data together to drive insights and deals. Product is currently in MVP state and requires development to a SOC II production grade platform for early customers. Ideally, this role becomes full time as part of the founding team presenting to investors later in the year. Must have an understanding and appetite for startups and working in an unstructured environment with a build mentality. Part time is an option for Sr. experienced engineers who have the capacity to execute rapidly due to their experience. Equity and salary will be discussed. Founder is in NY, NJ, but open to locations. Remote role.

We're a small SaaS company and we have a CSV dataset (~10,000 rows) of customer activity data including features like subscription length, login frequency, support tickets filed, monthly spend, and whether the customer churned (binary label). We need someone to build a simple but effective churn prediction model and expose it via a lightweight API so our internal tools can call it. Scope of work: - Perform basic exploratory data analysis (EDA) and generate a few key visualizations (churn distribution, feature correlations, top predictive features) - Clean and preprocess the data (handle missing values, encode categoricals, scale features) - Train and evaluate at least 2-3 classification models (e.g., Logistic Regression, Random Forest, XGBoost) with appropriate metrics (accuracy, precision, recall, F1, AUC-ROC) - Select the best model and save it as a serialized file (pickle or joblib) - Build a simple FastAPI endpoint that accepts customer features as JSON input and returns a churn probability score - Provide a Jupyter notebook with the full EDA + modeling workflow, plus the FastAPI app code - Include a brief README with setup instructions Deliverables: GitHub repo or zip with notebook, model file, API code, requirements.txt, and README. We're looking for someone who can do this quickly and cleanly — no over-engineering needed, just solid ML fundamentals and a working API. Ideally completed within a couple of days.