Join our immigration law firm in NYC to co-present a 15-minute government API demo. We need a Python/Flask developer to showcase our custom CRM's capabilities. This is a short-term engagement ideal for someone with experience in API integration and presentation. The role involves collaborating with our team to effectively demonstrate the API's features to a government audience.

I work as a project manager for a small group - part time. This is a group of Contract Specialists that complete a number of contract actions on a daily basis. Every month we have a daunting task of manually creating reports - it's awful. Its inefficient and time consuming. I started working with chat GPT to develop an automated system. It created a mockup, prototype farmwork that was awesome, but now it's getting way over my head in coding stuff I know nothing about. I'm lost.

We're looking for an experienced full-stack developer to join our team in building an AI-powered SaaS platform. You'll work on an existing production codebase, developing new features, improving performance, and integrating AI capabilities. This is a long-term opportunity for someone who writes clean, maintainable code and enjoys solving challenging engineering problems. Responsibilities Develop and maintain React frontend features. Build scalable REST APIs using FastAPI. Design and optimize PostgreSQL schemas and queries. Integrate AI/LLM services into existing workflows. Improve application performance, security, and reliability. Participate in code reviews and architectural discussions. Write clean, well-tested, production-quality code. Required Qualifications 5+ years of professional React experience. Strong Python and FastAPI experience. Excellent SQL/PostgreSQL knowledge. Experience with Docker-based development. Experience integrating third-party APIs. Strong Git workflow and communication skills. Able to work independently with minimal supervision. Nice to Have Experience with OpenAI or other LLM APIs. Redis and background job processing. AWS deployment experience. CI/CD pipeline experience. SaaS product development experience. To Apply Please include: 1. A brief introduction about yourself. 2. Links to your GitHub, portfolio, or live projects. 3. One or two React/Python projects you've built that are most relevant. 4. Your availability (hours/week). 5. Your hourly rate. Important: Please begin your proposal with "FastAPI + React" so we know you've read the entire job post.

I am looking for a Python/full-stack dev for a short paid gig (1-2 weeks) for my startup, Vera. We've got a working prototype (Next.js frontend + a solid, tested Python core) that needs three things: 1) confirm an LLM extraction step actually works against the Anthropic API, 2) wrap the core in a lightweight FastAPI layer, 3) deploy (Render/Railway + Vercel). Codebase is clean and Claude Code friendly. I am looking for someone who could also be interested, if the fit is right, in being a technical cofounder!

Python Developer Needed for Custom Local Document Automation & Quote Generator Job Description: I am looking for an experienced Python developer to build a custom, local desktop tool (or simple local web app) to automate my daily product quotation workflow. The Problem: I generate custom quotes for products daily, which currently takes me about 30 minutes per quote. I have an archive folder of over 2,500 past quotes ([PDF and Word versions or each quote) containing detailed technical product descriptions, images of the product, price and a specific layouts that I want to use. What the Tool Needs to Do: User Interface: Provide a simple, clean interface ~ local on my Windows PC or browser based, whatever is easiest (I prefer m local directory) but I don't really care ... that does the following: First ... using the example quote attached, create a similarly styled template which the following information can be "pulled into". 1. User inputs Customer Name, Company Name, Current Date, and Quote Number. 2. User inputs part number(s) that they want to add into the quote template. 3. SW searches & extracts from my archive folder of 2,500+ past quotes ~ finds the MOST RECENT quote with the matching part number(s) based on the past quote date, 4. SW extracts product information and ports into our new WORD quote template. 5. SW populates extracted info along with new customer details into WORD template that I currently use. Note that there is a product image in the old quotes that will have to be pulled over. Required Skills: -- Python (strong scripting and text processing) -- PDF/Word parsing and generation (e.g., pdfplumber, python-docx, ReportLab) -- Simple GUI development (Streamlit, PyQt, or Tkinter) Please reply with a brief description of a similar document parsing or automation tool you have built.

**Project Overview:** I am looking for an expert developer to build a lightweight desktop stock ticker application (Windows/macOS preferred) where the MAIN focus is high-utility, fully customizable AUDIBLE alerts. I want to monitor the markets by ear without constantly staring at my screen. The app will feature a customizable "Quote Builder" layout running on fast user-defined refresh loops, but the sound engine is the absolute priority of this project. **Core Audio Requirements (The Main Point):** * Event-Driven Sound Profiles: I need to assign distinct, custom text-to-speech (TTS) speeds, pitches, or triggers based on user-defined price movements. * Directional Audio Logic: Distinctly different tone pitches or speech profiles for "Up" ticks versus "Down" ticks so I can instantly hear market direction. * Speech Profiles: A drop-down menu to toggle between "Standard Mode" (reads full labels: "Tesla 100, up 2, bid 99...") and "Pro Mode" (strips all labels for high-speed tracking: "TSLA, 100, up 2, 99..."). * Global Panic Mute: Hitting the Spacebar or a dedicated hotkey must instantly mute/unmute all active audio feedback immediately. * API Key Settings & Data Feeds: The app must use a "Bring Your Own Data Feed" architecture. It must feature a configuration settings screen where users input their personal, API credentials (keys and tokens) to feed data into the ticker. * Brokerage Dropdown Selector: The UI must include a simple drop-down menu allowing users to choose which data provider or brokerage connection to activate (e.g., [Dropdown: Alpaca Markets, Polygon.io, Interactive Brokers, Yahoo Finance]). The developer must build modular data adapters for these connections. **Data & Interface Requirements:** * Custom Quote Builder: Ability to save layout templates choosing from fields like Symbol, Last Price, Up/Down, Bid/Ask Size, Day High/Low, Open, and Close. * Fast Polling Loops: Drop-down selector for data intervals per ticker: 1 second, 5 seconds, 30 seconds, 1 minute, or 5 minutes. * Multi-Monitor Support: Global hotkeys to switch saved templates instantly without needing the app window to be in active focus. * Ticker Looping: Supports inputting a single ticker or a comma-separated list to cycle through multiple stocks on the interval loop. **Budget & Contract Setup:** * Contract Type: Fixed-Price * Total Project Budget: $3,000 (To be broken into milestones upon signing an NDA) ⚠️ CRITICAL: You must start the very first word of your cover letter/proposal with the word "TICKER" to prove you are a human and not an automated bot. If your proposal does not start with the word "TICKER", it will be instantly declined without review. Please reply by explaining your experience with asynchronous programming, audio-based desktop systems, or handling high-speed financial APIs (like Alpaca or Polygon.io). Selected candidates will be asked to sign an NDA before receiving the full requirements document.

Python Developer Needed – Claude API, PDF Processing & AI Automation I’m looking for an experienced Python developer to help me complete and troubleshoot an AI document-processing workflow. I have approximately 20,000 pages of OCR’d medical records that need to be processed using the Claude (Anthropic) API. The goal is to identify specific mental health evidence while ensuring every extracted finding includes the exact PDF filename and page number so it can be verified and used in court. Current Status * Claude API account is already set up and paid for. * PDFs are already OCR’d and page numbered. * I have an existing Python script that uploads documents and processes them, but it needs improvement. What I Need * Debug and improve the existing Python code. * Ensure page numbers and source filenames are always included. * Improve reliability and error handling. * Optimize processing of a large document set. * Help generate a final organized evidence list. Required Skills * Python * Claude (Anthropic) API or other LLM APIs * API integration * PDF/document processing * OCR workflows * Prompt engineering * Debugging and automation Experience working with legal, medical, or other large document collections is a plus. This is an urgent project, and I’m looking for someone who can start immediately. When applying, please include: * Similar projects you’ve completed * Your experience with Python and AI APIs * Your hourly rate * Your availability over the next couple of days I’m in Las Vegas

We are seeking an experienced Healthcare Data Analyst / AI Developer to analyze patient data and build an insights platform that turns complex healthcare datasets into actionable intelligence. The ideal candidate will have strong experience working with EHR, EMR, claims, or clinical data and building scalable analytics workflows. You will clean and transform healthcare data, identify meaningful trends, create dashboards, and develop predictive models to support better operational and clinical decisions. Strong Python, SQL, data visualization, and machine learning skills are required, along with experience handling healthcare data securely and following HIPAA best practices. Experience with FHIR/HL7, cloud healthcare platforms, healthcare SaaS products, and LLM-based analytics solutions is a plus. Deliverables will include data exploration reports, structured datasets, analytics dashboards, predictive models where applicable, and clear documentation of findings. This is a long-term, part-time opportunity with a budget of $5k per month depending on experience and project scope.

Overview I am looking for an experienced Python developer to build a stand-alone desktop research application for futures trading strategy analysis. This is not an automated trading bot and does not require live trading execution. The purpose of this software is to replace manual backtesting and allow systematic research of Opening Range Breakout (ORB) strategies. The application will allow a trader to quickly test strategy variations, compare results, and identify robust parameters without manually running hundreds of backtests. Accuracy of results is the highest priority. Project Goal Build a desktop application where the user can: Select a futures market Load historical data Configure ORB strategy parameters Run single tests or multiple parameter combinations Analyze results Compare experiments side-by-side Save research results The software should be simple and user-friendly. Platform Stand-alone desktop application. Primary requirement: Windows Desired: macOS compatibility The user should not need: TradingView Excel FX Replay Coding knowledge The software should open like a normal desktop application. Supported Markets (Version 1) The architecture should support: Nasdaq Futures NQ MNQ S&P 500 Futures ES MES The system should properly handle: Tick size Tick value Contract specifications The design should allow additional futures markets to be added later. Data Requirements Historical Data Integration with: Databento API Requirements: 1-minute historical data User-selectable date ranges Ability to build higher timeframe candles from 1-minute data Supported research candles: 1 minute 3 minute 5 minute 10 minute 15 minute ORB Strategy Engine Standard ORB User can select: 1 minute 3 minute 5 minute 10 minute 15 minute opening range Dynamic ORB (Anchor ORB) User can select: 1 minute 3 minute 5 minute 10 minute 15 minute Logic: The first candle that closes outside the opening range becomes the new ORB anchor. The closing price of that candle becomes the reference level for entries. Entry Types Version 1 supports: Breakout entry Dynamic ORB anchor entry Stop Loss Testing User can test: 25% 33% 50% 66% 75% 100% Stop size is based on ORB size. Profit Target Testing The software must support testing multiple R targets: From: 0.5R to 10R In: 0.5R increments Example: 0.5R 1R 1.5R 2R etc. Risk Management Support: Fixed Dollar Risk Example: $100 $250 $500 Percentage Account Risk Example: 0.5% 1% 2% Filters ORB Size Filter User selectable: Minimum: 0.10% Maximum: 2.00% Day of Week Filter Allow testing: Monday Tuesday Wednesday Thursday Friday News Filters Option to exclude: High-impact economic news days FOMC days Federal Reserve Chair speech days Research Engine The software must support: Single Backtest Run one specific strategy configuration. Multi-Variable Testing Allow combinations of: Market ORB duration ORB size Entry type Stop size Profit target Day filters News filters Example: Test: 10 ORB sizes 6 stop sizes 20 profit targets Multiple markets Automatically generate and run experiments. Parameter Locking Important feature: The user must be able to lock certain parameters while testing others. Example: Lock: Entry type Risk model Optimize: ORB size Stop Target This prevents unnecessary over-optimization. Results Dashboard Display: Performance Metrics Net Profit Profit Factor Expectancy Win Rate Total Trades Average Winner Average Loser Maximum Drawdown Largest Winning Streak Largest Losing Streak Charts Required: Equity Curve Drawdown Curve Experiment Comparison Allow side-by-side comparison. Example: Strategy A vs Strategy B Compare: Parameters Profit Factor Expectancy Drawdown Trade count Win rate Saving Research Users should be able to: Save experiments Reopen experiments Save notes Technical Preferences Preferred: Python backend Open to developer recommendations for: Desktop framework Database Architecture Experience preferred with: Financial applications Backtesting systems Time-series data Quantitative research tools Important Developer Qualifications Please have experience with: Event-driven backtesting Historical market data Avoiding look-ahead bias Accurate trade simulation Parameter optimization This project is research-focused. A simple candle backtester is not sufficient. Application Requirements Please provide: Examples of similar work GitHub or portfolio links if available Recommended technology stack Estimated timeline Fixed-price estimate Budget Expected MVP range: $4,000–$7,000 (depending on experience and recommended architecture) This project may expand into future versions after successful completion. Final Note The goal is to build a reliable research tool that allows systematic testing of futures strategies. The first version should prioritize: Accuracy Simplicity Ease of use Clean architecture for future expansion

We're building a web-based Python/SaaS assessment tool that helps enterprises understand their risk exposure across the rapidly evolving AI regulatory landscape — including AI-specific regulations, US state/federal AI laws) as well as AI applicable frameworks (e.g., NIST AI RMF). The platform combines a structured knowledge base with automated workflows to generate a company's AI risk score and actionable recommendations. We already have System Requirements Specifications (SRS) documentation. Skills: Python, Django, FastAPI, PostgreSQL, React, SaaS Architecture, Multi-Tenant Systems, Workflow Automation, API Development,