Full-Stack / Data & AI Engineering Partner for Real-Time Sports Betting Platform

Full-Stack / Data & AI Engineering Partner for Real-Time Sports Betting Platform We are looking for an experienced Full-Stack, Data & AI Engineer / Technical Partner or Development Team to design and build a real-time sp’orts betting analytics and intelligence platform from the ground up. The platform will aggregate live sports and sportsbook data, normalize high-frequency odds feeds, generate betting intelligence and market signals, track betting performance, and gradually evolve toward predictive AI, Generative AI, and personalized betting intelligence. We are looking for someone who can own the complete lifecycle: Discovery → Architecture → Technology Selection → MVP Development → Deployment → Optimization → Data/AI Roadmap We do not want to prescribe every technology upfront. We expect the selected technical partner to recommend the right stack and explain the reasoning based on scalability, latency, cost, maintainability, data volume, and future AI requirements. Phase 1 — MVP & Core Platform The initial goal is to build a production-ready MVP covering: User registration, authentication, MFA/2FA and role-based access Sports, leagues, events, markets and sportsbook management Integration with SportsGameOdds (SGO), The Odds API, SportsRadar or similar providers Multi-provider data normalization and event mapping Real-time odds ingestion and updates Sportsbook and market comparison +EV calculations Arbitrage detection No-Vig / fair probability Kelly Criterion CLV tracking Market movement and betting signals Bet tracking, grading and settlement Bankroll, ROI and performance analytics User dashboard Admin dashboard Stripe subscriptions and feature-based access CMS / educational content section API monitoring, data validation and anomaly detection Multi-provider normalization, duplicate/out-of-order event handling and low-latency odds delivery are expected to be important architectural considerations for the platform. These were also core challenges in the reference solution architecture. Phase 2 — Advanced Data & Betting Intelligence After the MVP, we want to expand the platform with: Historical odds and line-movement analysis Steam and reverse-line movement Sharp-money indicators Market sentiment Sportsbook consensus Strategy backtesting Advanced bankroll analytics Opportunity/risk scoring Automated strategy evaluation Data warehouse / lakehouse Historical analytics datasets Advanced data-quality and reconciliation workflows The system should be designed from the beginning so transactional workloads, real-time processing and historical analytics can scale independently. Phase 3 — Predictive & Generative AI A future phase will introduce more advanced AI capabilities such as: Game / market outcome prediction Player and team performance models Closing-line prediction Market movement forecasting Personalized betting recommendations Dynamic confidence scoring Sportsbook pricing anomaly detection AI-assisted strategy creation Conversational betting assistant Natural-language analytics RAG over historical sports data, betting strategies and educational content Semantic / vector search AI-generated explanations for betting recommendations We expect the technical partner to advise where deterministic mathematics, rules engines, statistical modeling, machine learning, or Generative AI are most appropriate rather than applying AI unnecessarily. Technology Stack — Open to Recommendation We want the selected partner to evaluate and recommend the final architecture. Relevant technologies may include: Frontend: React.js, Next.js, TypeScript, Tailwind CSS, Material UI Backend: Node.js, NestJS, Express.js, Python, FastAPI, REST APIs, GraphQL Databases: PostgreSQL, MySQL, Redis, TimescaleDB, ClickHouse, MongoDB Vector Databases / Search: pgvector, Pinecone, Qdrant, Weaviate, OpenSearch / Elasticsearch Real-Time & Messaging: WebSockets, Socket.IO, Kafka, AWS SQS/SNS, Kinesis, RabbitMQ, Redis Pub/Sub / Streams Data Engineering: Python, Pandas, PySpark, Airflow, dbt, AWS Glue, Databricks, ETL/ELT pipelines Data Warehouse / Lakehouse: Amazon Redshift, Snowflake, BigQuery, Databricks Lakehouse, Delta Lake, S3 Data Lake Generative AI: OpenAI, Azure OpenAI, Gemini, Claude, LangChain, LangGraph, LlamaIndex, embeddings, RAG, tool/function calling Machine Learning: Scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow, MLflow, SageMaker, Databricks ML Cloud: AWS preferred, but Azure or GCP recommendations are welcome. Potential AWS services may include: ECS / EKS, Lambda, S3, RDS, ElastiCache, SQS, SNS, Kinesis, API Gateway, CloudFront, Secrets Manager, CloudWatch and SageMaker. DevOps / Infrastructure: Docker, Kubernetes, Terraform, GitHub Actions, GitLab CI/CD, AWS CodePipeline, Ansible Security: OAuth2, JWT, SSO, MFA/2FA, RBAC, rate limiting, encryption, Secrets Manager, audit logging, OWASP best practices Observability: CloudWatch, OpenTelemetry, Prometheus, Grafana, Datadog, Sentry The reference architecture uses PostgreSQL/Redis, queue-based processing, WebSockets, AWS infrastructure, Docker, Terraform and CI/CD patterns, illustrating the type of production-grade architecture we are considering. What We Expect From the Technical Partner We are specifically looking for someone who can: Understand the business and betting domain Refine technical requirements Recommend the right architecture and technology stack Explain what technology should be used and why Design the application and data architecture Build the MVP end-to-end Develop real-time and asynchronous processing Design scalable data pipelines Set up cloud infrastructure and CI/CD Implement security and monitoring Optimize performance and infrastructure costs Create a roadmap for predictive AI and Generative AI Continue supporting the product through future phases Experience building real-time analytics, FinTech, trading, sports data, high-frequency API, SaaS, Data Engineering or AI platforms will be highly relevant. Engagement We would like to start with: Technical Discovery + Architecture + MVP Development and, based on successful delivery, continue into: Advanced Betting Intelligence → Data Platform → Predictive AI/ML → Generative AI & Personalized Intelligence This is intended to be a long-term technical partnership, not a one-time development task. When applying, please briefly share: How you would approach the architecture. Which core technology stack you would recommend and why. How you would handle high-frequency multi-provider sports data. How you would design the platform today so predictive AI/ML can be introduced later. Relevant examples of real-time, Data Engineering, SaaS or AI platforms you have built.