AI/ML Engineer, Conversational AI

Fanatics•New York, NY

102d

About The Position

Fanatics Markets is the real-money prediction and trading app where you can invest in moments you care about. Built on a secure platform, we let users predict real-world outcomes and trade on events they actually follow - from sports and entertainment to political elections and beyond. Our mission is to redefine how fans engage with the moments and markets that matter most. We're looking for the right people to help us build the future of prediction markets. AI/ML Engineer, Conversational AI Fanatics Betting and Gaming is seeking an AI/ML 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 autonomous agent capabilities to internal stakeholders and 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.

Requirements

7 years of professional software engineering experience, with at least 2 years focused on AI/ML systems such as conversational AI, customer support, copilot and/or chatbot experiences
Hands-on experience building and deploying models in production
Proficiency in Python (FastAPI, Flask) and familiarity with strong-typed languages (Java, Go, or C#) for microservice development
Experience 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 pipelines
Comfortable navigating ambiguity in a startup-like environment: you’re resourceful, rapid in iteration, and thrive on end-to-end ownership

Responsibilities

Engineer Conversational ML for Support Tools
Design, fine-tune, and productionize dialogue models—integrating RAG pipelines, context-aware routing, and continuous feedback loops—to power internal workflows that supercharge customer-support agents while hitting sub-100 ms latency and enterprise-grade reliability.
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.