Looking for a Python developer with sports analytics experience to build a college football grading engine. The full specification includes 34 custom metrics, requiring a deep understanding of football analytics and data modeling. The project involves creating a grading engine that can process large datasets efficiently and accurately. Ideal candidates will have experience with data visualization and machine learning.

Overview We are looking for an experienced Python developer with strong technical expertise and exceptional communication skills. This role is ideal for someone who is comfortable discussing technical concepts with clients, participating in interviews, and collaborating closely with stakeholders. We value developers who can not only write clean, scalable code but also clearly explain their thought process, ask the right questions, and represent our team professionally during client meetings and technical interviews. Responsibilities - Design, develop, and maintain Python applications and backend services. - Build and integrate APIs, databases, and third-party services. - Participate in technical discussions with clients and internal teams. - Attend interviews with excellent verbal communication. - Write clean, maintainable, and well-tested code. - Troubleshoot and optimize existing systems. Required Skills - 5+ years of professional Python development experience. Strong knowledge of: Python, FastAPI, Django, or Flask, REST APIs and microservices, PostgreSQL, MySQL, or MongoDB, AWS, Docker, and CI/CD practices - Experience with system design and scalable architectures. - Excellent English communication skills (written and spoken). - Comfortable participating in technical interviews and client-facing discussions. - Ability to explain technical concepts clearly to both technical and non-technical stakeholders. Nice to Have - Experience with cloud infrastructure and DevOps practices. - Experience with AI/ML integrations or data pipelines. - Previous consulting or agency experience. - Experience working with distributed remote teams.

We are looking for a hands-on senior Python/FastAPI trading-system developer with strong testing, debugging, and broker API experience. This person should be able to work inside an existing codebase, identify what is breaking, fix the issues, and prove the fixes with tests, logs, API output, and dashboard evidence. Ideal experience: • Python backend development • FastAPI • MongoDB • Heroku logs and deployment workflow • React/TypeScript frontend debugging • Alpaca API or similar broker API experience • Trading bot lifecycle experience • Automated testing and regression testing • Market data, candle/bar data, indicators, and timestamp handling • Ability to trace scanner → scoring → decision → order readiness → position state → sell/hold logic This is not a rebuild project. The goal is to test and repair the current system.

Experienced Dev Ops from start to finish, We are seeking a skilled Python and React Developer Expert for Web Based Trading Application Platform to create a robust and user-friendly application for both web and mobile users. The ideal candidate will have experience in developing and launching apps from start to finish. Knowledge of stocks and options is necessary or trading experience is a MUST. If you do not know how to trade stocks or options, you will be required to take a strategy class (fee paid by the applicant freelancer) with founder owner, to learn how the trading strategy works, in-order to be hired. Deliverables • Ensure existing application functionality and performance as per founder owners requirements • Strategy is proprietary and is confidential for period of 30 years • All coding is live over anydesk each day, all coding sessions are recorded over zoom

We are building an early-stage real estate data platform that collects, cleans, enriches, and serves public-record and legal-notice data for real estate investors and professionals. This is not a greenfield build. We already have an existing backend repo with API routes, database models, migrations, scraping workers, tests, Docker configuration, and cloud deployment pieces. We need a strong backend engineer who can step into the existing system, understand what is working, identify what is risky, and help us get the backend stable enough for launch. The right person is practical, scrappy, and comfortable working in a startup environment where the goal is not perfection. The goal is to find the highest-leverage path to a reliable product. The platform involves: -Public-record and legal-notice data -Property data enrichment -API endpoints used by a frontend application -Data quality, reliability, and launch-readiness Current Backend Stack The backend is built primarily in Python and includes: -FastAPI -SQLAlchemy and Alembic -Postgres / Google Cloud SQL -MongoDB helper/caching layer -Scraping and ETL pipeline for public-record and legal-notice data -Playwright/Patchright-based scraping -reCAPTCHA-aware scraping workflows -LLM-based data extraction / AI-assisted parsing of unstructured notice data -Pydantic models -Google Cloud integrations: Cloud Run, Cloud Scheduler, Pub/Sub, Secret Manager, Cloud Storage, Artifact Registry -Docker -Pulumi infrastructure-as-code -GitHub Actions CI/CD -pytest, Ruff, uv You do not need to be world-class in every tool listed above, but you should be strong enough in Python backend systems, scraping/data pipelines, and cloud deployment to quickly understand the architecture and make sound technical decisions. What We Need Help With We need someone who can: -Review and understand the current backend architecture -Stabilize and improve the scraping / ETL pipeline for public-record and legal-notice data -Make sure public-record and legal-notice data is collected, parsed, stored, and served correctly -Improve backend APIs used by the frontend -Improve data quality checks for incomplete, missing, or inconsistent property records -Build and maintain property enrichment workflows using external data sources -Help design database models for richer property history and event tracking -Improve LLM-assisted parsing of unstructured legal notice data where appropriate -Debug deployment, CI/CD, Cloud Run, and infrastructure issues -Improve logging, error handling, monitoring, and observability -Strengthen test coverage where it matters -Help document the backend so future developers can contribute -Coordinate with our frontend developer to support product launch -Help prioritize backend work based on launch impact, data reliability, and technical risk Who This Is For You are likely a strong fit if you: -Like working inside existing codebases -Can diagnose messy systems without needing everything rewritten -Think in practical tradeoffs, not just ideal architecture -Are comfortable with incomplete documentation -Have experience with scraping/ETL workflows and unstructured data extraction -Can explain technical risks clearly to a non-technical founder -Prefer shipping useful improvements over debating perfect abstractions -Are willing to own outcomes, not just complete assigned tickets Who This Is Not For This is probably not the right fit if you: -Only want clean, fully documented codebases -Prefer to rebuild from scratch by default -Need enterprise-level process before making progress -Are an agency sending rotating developers -Only want tightly defined tickets with no ambiguity -Are uncomfortable with scraping, data quality, or production debugging Hiring Process We want to keep the hiring process practical and focused on real work. 1. Initial Screening We will review your proposal, background, and screening question responses. 2. Real-World Technical Scenario Strong candidates may be asked to respond to a specific backend issue from our current roadmap. We are looking for how you think, what tradeoffs you notice, and how clearly you communicate. 3. Paid Finalist Review A small number of finalists may be invited to complete a paid review of the existing backend codebase before any larger implementation work begins. Budget / Working Style We are an early-stage company and are looking for a practical, startup-minded developer. This is a paid contract role, but we are not looking for enterprise-agency rates. We value clear communication, efficient execution, and someone who can help us prioritize the highest-leverage backend work first. The first paid technical review may be structured as a fixed-price milestone. Continued implementation work may be hourly or milestone-based depending on fit. Long-Term Opportunity Our goal is to find someone who can become a long-term backend partner for the product, not just complete isolated tickets. For the right person, there may be an opportunity to grow into a technical lead / backend ownership role with additional upside tied to company performance. We are looking for someone who wants to help take a real product to market, but the initial engagement will be paid, scoped clearly, and focused on proving mutual fit.

**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.

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

Project Overview: I am seeking an expert Python developer to build a lightweight, local automation script that bridges custom TradingView indicator alerts to Interactive Brokers (IBKR) for automated options execution. Core Deliverables: Webhook Listener: A local Python script that securely listens for JSON payloads sent via TradingView webhooks. Dynamic Option Chain Scanner: Upon receiving a payload containing specific parameters (Ticker, Target Delta, DTE), the script must query the live IBKR option chain (using ib_insync or ib_async), find the exact or closest available strikes matching that Delta, and calculate secondary strikes based on distance to the current spot price. Complex Multi-Leg Order Routing: The script must dynamically assemble these strikes into a specific 5-leg complex combo order (combining a credit vertical spread, two debit vertical spreads, and a naked wing) and route it to the market as a single transaction using mid-price algorithmic fills. Rigorous Error Handling & Logging: The code must include strict safeguards (rate limits, bid/ask spread checks, and fallback logic if an exact strike or delta is unavailable) to ensure no rogue orders are fired. Developer Requirements: Demonstrated expertise with the Interactive Brokers API (ib_insync framework heavily preferred) and TWS/IB Gateway. Deep understanding of options mechanics, including how to handle complex multi-leg combinations and delta sourcing. Excellent, proactive communication. Weekly availability during standard New York market hours (EST) for live testing. Must be located in the United States (required for IP protection and NDA enforcement). Workflow & Milestones: Initial development and testing will be done exclusively using an IBKR Paper Trading account. The project will be managed via two strict milestones: 1) Full execution validation in a paper environment, and 2) Live market deployment verification using a single test contract. If you have read this entire post and have experience with IBKR options routing, please start your proposal with the word "DELTA" and briefly describe a past algorithmic trading project you have built.

We're hiring a senior AI developer to build and deploy AI solutions for a fintech/credit-union platform. The work spans autonomous banking agents, fraud detection, credit scoring, and bill-pay/invoice automation — at the intersection of LLMs, cloud infrastructure, and financial-domain expertise, with security and compliance built in from the start. This is a long-term, ongoing engagement. What you'll do: AI agents & orchestration - Design, build, and deploy multi-agent systems using Amazon Bedrock Agents, LangChain, and related frameworks - Architect agentic workflows for core banking use cases: credit scoring, fraud detection, bill-pay automation, invoice management - Define agent personas, memory strategies, tool-use patterns, and escalation paths for production banking agents LLM engineering - Fine-tune, prompt-engineer, and evaluate LLMs for financial-domain tasks - Build RAG pipelines over credit-union knowledge bases, policy docs, and member data - Implement guardrails, content filtering, and compliance checks for safe, regulated outputs - Monitor performance, hallucination rates, and latency against SLAs Cloud infrastructure (AWS & Azure) - Architect and manage AI/ML workloads on AWS (Bedrock, SageMaker, Lambda, S3, IAM, VPC) and Azure (OpenAI Service, Azure ML, AKS) - Design secure, cost-optimized environments compliant with NCUA, PCI-DSS, and SOC 2 - Implement infrastructure-as-code with Terraform or AWS CDK DevOps & MLOps - Build and maintain CI/CD pipelines (GitHub Actions, Jenkins, CodePipeline, Azure DevOps) - Containerize services with Docker, orchestrate with Kubernetes (EKS/AKS) - Apply MLOps best practices: model versioning, A/B testing, canary deployments, automated rollback - Stand up observability with logging, tracing, and alerting Python development - Write clean, well-tested Python for AI pipelines, REST APIs, and data workflows - Build FastAPI/Flask microservices exposing agent capabilities to frontend and core banking systems - Integrate with financial data sources, core banking APIs, and third-party fintech services Banking applications - Build credit-scoring models using alternative data and explainable AI (XAI) - Develop real-time fraud detection with behavioral analytics, anomaly detection, and auto-decisioning - Create conversational agents for bill pay, account management, and member self-service - Automate invoice workflows: extraction, classification, approval routing, reconciliation - Partner with compliance/risk to keep AI decisions auditable, fair, and regulatory-compliant What you should have: - 5+ years software engineering; 3+ years in AI/ML or LLM engineering - 2+ years building AI for banking, credit unions, or financial services - Hands-on experience with Amazon Bedrock, LangChain, Python, AWS, and infrastructure-as-code - Working knowledge of NCUA, PCI-DSS, SOC 2, GLBA, and Fair Lending requirements - Bachelor's or Master's in Computer Science, Software Engineering, Data Science, or related field Nice to have: - AWS or Azure AI/ML certifications - Open-source LLM experience (Llama, Mistral, Phi) and self-hosted inference (vLLM, Ollama) - Vector databases (Pinecone, OpenSearch, pgvector) - Graph-based fraud networks and graph ML - AI governance / responsible AI framework experience - Prior work at a credit union, community bank, or fintech lending platform To apply, please share: - Your resume highlighting AI and banking project experience - A brief note on your most impactful AI agent or LLM project in a financial-services context - Links to GitHub, portfolio, or published papers (optional but encouraged)

We are looking for a strong software engineer who can build practical automation systems using AI, APIs, and modern development tools. This role is for someone who can take messy business workflows, understand the goal, and build working systems that save time, reduce manual work, and improve execution. You should be comfortable building automations, integrating tools, working with APIs, writing clean code, and using AI tools like OpenAI, Claude, or similar models to create useful business applications. What You’ll Work On You will help build and improve systems such as: AI-powered research and data extraction workflows CRM and sales process automations Email, spreadsheet, and database automations Internal tools and dashboards API integrations between business software Web scraping and data enrichment workflows when appropriate AI agents or assistants that help with repetitive business tasks Automation around deal screening, reporting, lead research, and document creation Ideal Candidate We are looking for someone who is practical, fast, and can figure things out without needing step-by-step instructions. You should have experience with: Python and/or JavaScript APIs and webhooks OpenAI, Claude, or other LLM APIs Automation tools like Zapier, Make, n8n, Airtable, Google Sheets, HubSpot, Salesforce, or similar Databases such as PostgreSQL, Supabase, Firebase, or similar Basic front-end or internal tool development Web scraping, data cleaning, and structured data workflows GitHub and clean documentation What Matters Most We do not need someone who only talks about AI. We need someone who can actually build. The right person should be able to: Understand a business process quickly Recommend the simplest technical solution Build fast prototypes Turn prototypes into reliable workflows Communicate clearly Document what was built Improve systems over time Nice to Have Experience with any of the following is a plus: Private equity, M&A, finance, or investment workflows Deal sourcing or lead generation systems CRM automation Data enrichment tools AI research agents Browser automation Cloudflare, AWS, Google Cloud, or similar infrastructure Engagement This will start as a part-time project-based role, with the potential to become ongoing if the work is strong. Estimated workload: 5 to 15 hours per week to start. To Apply Please include: Examples of automations or AI tools you have built The tech stack you usually work with A brief explanation of how you would approach automating a messy manual workflow Your hourly rate Your availability Please do not send a generic application. If your response looks copied and pasted, it will be ignored.