Applied AI Engineer

About the role

The New York Mets are seeking an Applied AI Engineer to build and ship production-grade AI capabilities that improve decision-making and streamline workflows across Baseball Operations. This role sits at the intersection of Baseball Systems (software engineering), Data Engineering, Baseball Analytics, Performance Technology, and modern generative AI. You will develop reliable AI-powered applications such as retrieval-augmented generation (RAG), secure tool/data integrations (MCP-style patterns), and agentic workflows that can support deep work and research, while ensuring strong standards for quality, security, privacy, and operational excellence. This is a hands-on role for an engineer who can translate ambiguous baseball operations and player development needs into working software, iterate quickly with stakeholders, and harden solutions into durable systems used daily by analysts, coaches, scouts, and baseball operations staff. Note : This role will require extensive in-person collaboration and innovation alongside stakeholders, analysts, and engineers. Applicants must be local to NYC (or willing to relocate) and be able to travel to Citi Field regularly. Travel to Spring Training and affiliates may also be required. Essential Duties & Responsibilities Design, build and maintain AI-powered product experiences that provide intuitive access to baseball information and statistics and workflows across internal systems. Develop end-to-end RAG pipelines (ingest, chunking, embedding, retrieval, generation) with strong attention to answer quality, baseball relevancy, analytics accuracy, latency, and cost. Implement secure tool and data connectors for AI assistants and agents using standardized patterns (MCP-style), enabling safe interaction with internal APIs, data warehouses, and services. Build agentic workflows that can execute multi-step tasks (research, analysis, synthesis, report generation) with clear guardrails, auditability, and human-in-the-loop approvals. Partner closely with Product, Analytics, Performance Technology, Player Development, and Baseball operations stakeholders to frame problems, define success, and deliver measurable impact. Contribute to shared engineering standards and reusable components so AI capabilities scale across multiple products and teams. Ensure strong security and privacy practices (access control, data boundaries, logging and auditing, and safe handling of sensitive information). Participate in high-availability support expectations as needed during critical operational periods throughout the baseball season, and during feature releases.