Senior Python Trading Systems Developer — Scaling Kalshi/Polymarket RFQ and Automated Trading

# Senior Python Trading Systems Developer — Kalshi RFQ Quoter Experience with Kalshi, prediction markets, RFQs, market making, sportsbook odds, same-game parlays, or exchange order-management systems is strongly preferred. ## Overview I operate a professional sports betting and prediction-market business. I have spent the past year developing a Python application that trades on Kalshi on straight markets. My verified Kalshi trading results can be viewed on their site here: https://kalshi.com/ideas/profiles/runbadruben I am hiring an **individual senior Python developer** to build a standalone service that receives, prices, and manages Kalshi sports-combo Requests for Quote. The initial goal is a working pregame MVP before the college football and NFL seasons begin in late August 2026. A successful initial project could become an ongoing development relationship. ## What You Will Build Kalshi users create multi-leg sports combinations and broadcast RFQs. The service will: * Receive RFQs through Kalshi’s WebSocket and REST APIs * Map every Kalshi combo leg to the correct sportsbook market * Price eligible same-game parlays using the OpticOdds SGP pricing API * Apply configurable margins and risk limits * Submit a quote on one side only, leaving us short the requester’s parlay * Reprice or cancel open quotes when prices or eligibility change * Reconcile executed orders, fills, fees, and positions * Maintain a complete append-only audit journal * Provide an operator dashboard and kill switch ## Main Engineering Challenges * Asynchronous Python * REST and WebSocket synchronization * RFQ and quote lifecycle state machines * Reconnect and missed-event reconciliation * Duplicate and stale-action prevention * Ambiguous-response handling without exchange idempotency keys * Reserved rate-limit capacity for three-second confirmations * Fixed-point or Decimal financial calculations * Exposure limits across parlays sharing the same legs * Sportsbook-to-Kalshi market mapping * Postponements, schedule changes, and doubleheaders * Safe kill-switch and fail-closed behavior Implementation will then proceed through small milestones: 1. Demo RFQ ingestion and journaling 2. Market-mapping engine and coverage reports 3. Full demo quote lifecycle 4. Limited production rollout at very small sizes 5. Gradual automation and cap increases Development begins in Kalshi’s demo environment. You will not receive production credentials, access to funds, or production-host access. ## Required Experience * Strong production Python experience * Asynchronous Python * REST and WebSocket integrations * Trading, brokerage, exchange, sportsbook, or financial APIs * Order-management or transaction state machines * Reconciliation, retries, race conditions, and partial failures * Fixed-point or Decimal financial calculations * Automated testing of money-moving behavior * Clear written communication ## To Apply Answer each question directly in numbered format: 1. Have you worked with Kalshi or Polymarket before? If so, what exactly did you implement? 2. Have you worked with RFQ's? If so, what exactly did you implement?. 3. Describe the most relevant live trading, exchange, brokerage, sportsbook, or financial system you personally built. Generic applications or applications that do not answer the questions will not be considered.