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Brooklyn Sports & Entertainment

AI Engineer

Brooklyn, NY 11232$125k–$150kmidAdded 1 month ago

About this role

Brooklyn Sports & Entertainment seeks an AI Engineer to build intelligent agent-based systems that automate business workflows across ticketing, operations, HR, and development. You'll work with AWS and Amazon Bedrock to create agentic applications using LangGraph and contribute to internal AI-assisted development tools.

What you'll do

  • Design and develop agent-based workflows to automate processes across ticketing, partnerships, HR, and venue operations
  • Build agent orchestration patterns using LangGraph and Amazon Bedrock with integrations to Salesforce and internal APIs
  • Implement reliable tool-calling patterns for safe agent execution of operational tasks
  • Develop CodeAI agents supporting engineering workflows including code generation, testing, and documentation
  • Participate in deployment, monitoring, and incident response for AI systems in production
  • Improve agent reliability through evaluation pipelines, observability metrics, and collaboration with LLMOps teams

What they're looking for

  • Python or TypeScript programming
  • AWS cloud services
  • LLM APIs and AI frameworks
  • LangGraph or agent orchestration tools
  • Amazon Bedrock
  • API integration and development
  • Production systems design
  • Incident response and monitoring
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Brooklyn Sports & Entertainment

Brooklyn Sports & Entertainment builds AI-powered platforms and software solutions that enhance decision-making and operations across its sports and entertainment properties. The company is hiring for AI infrastructure, full-stack development, analytics, and engineering roles focused on deploying production-ready AI systems, agentic applications, and operational tools used by coaches, scouts, and business teams.

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Likely interview questions

  • Tell us about a production AI or LLM system you've built. What frameworks did you use, and how did you handle reliability and monitoring?
  • Describe your experience building agent-based workflows. What orchestration tools or frameworks have you worked with, and what challenges did you encounter?