About this role

Fanatics Betting and Gaming is seeking an AI Engineer on our cross-functional Applied AI team that is on a mission to 10x FBG with AI. You’ll build and productionize the software foundations that power next-gen recommenders, autonomous AI agents, and scalable AI-driven experiences—working in a fast-paced, startup-style environment where you’ll move from prototype to ship-ready features in days, not months. As an AI Engineer at Fanatics, you will partner with Applied Scientists, Data Engineers, and Product teams to design, implement, and maintain end-to-end systems that deliver personalized recommendations and autonomous agent capabilities to millions of fans. You’ll translate cutting-edge research into rock-solid code, architect microservices for low-latency inference, and champion best practices in reliability, scalability, and observability.

Responsibilities

Design & Build Recommender Services
Architect and implement microservices that serve real-time personalized recommendations (e.g., odds suggestions, content feeds) at low latency and high throughput.Develop AI Agent Frameworks
Create, integrate, and extend agent orchestration pipelines—building tools to deploy, monitor, and iterate on multi-agent workflows.Collaborate in a Startup-Like Pod
Work hands-on across the stack: from data ingestion and feature engineering to model deployment and UX integration—rapidly prototyping and shipping proof-of-concepts alongside engineers and product managers.Optimize for Scale & Reliability
Implement CI/CD for model updates, containerize inference workloads (Docker/Kubernetes), and instrument services with logging, tracing, and alerts to meet 99.9% uptime SLAs.Champion Engineering Best Practices
Write clear, maintainable code; conduct code reviews; mentor junior teammates; and evangelize automated testing, code linting, and performance monitoring.
 

Skills & Qualifications

7+ years of professional software engineering experience, with at least 2 years focused on AI/ML systemsHands-on experience building and deploying models in productionProficiency in Python (FastAPI, Flask) and familiarity with strong-typed languages (Java, Go, or C#) for microservice developmentExperience with container orchestration (Docker, Kubernetes) and cloud platforms (AWS, GCP, or Azure)Knowledge of AI agents and frameworks (e.g. LangChain, Ray RLlib, custom orchestrators)Solid understanding of distributed systems, API design, and data pipelinesComfortable navigating ambiguity in a startup-like environment: you’re resourceful, rapid in iteration, and thrive on end-to-end ownership