AI Enterprise Architect at Peloton Interactive
ABOUT THE ROLE
Reporting directly into the CIO organization, you will be the lead architect and "ace technologist" responsible for the next era of Peloton’s internal evolution: Agentic AI. You will go beyond simple chatbots to build autonomous and semi-autonomous AI agents that execute complex workflows across Finance, Supply Chain, Marketing, Legal, and the People Team. This role is for a hands-on coder who can also set the strategic technical roadmap for enterprise-wide automation.
YOUR DAILY IMPACT AT PELOTON
Architect and code multi-agent systems that can reason, use tools, and execute end-to-end business processes (e.g., automated financial reconciliation, legal contract analysis, or supply chain demand forecasting)
Partner with leaders in Finance, Marketing, Legal, and HR to translate their complex operational challenges into concrete AI architectures
Evaluate the latest agentic frameworks and LLM providers to decide when to leverage third-party tools and when to build proprietary enterprise solutions
Design highly available inference services and manage model serving at scale, ensuring these agents integrate seamlessly with existing corporate service meshes
Implement "Security by Design," creating automated guardrails for PII/PHI filtering and mitigating prompt injection to ensure enterprise data remains secure
Optimize the "Total Cost of Ownership" by managing trade-offs between agent reasoning depth (latency) and compute costs through intelligent caching and specialized routing
YOU BRING TO PELOTON
8–10+ years in software engineering, with at least 2 years of deep focus on AI/ML infrastructure or data engineering at scale
Mastery of Python, Go, or Java/Scala, with a strong grasp of distributed systems and microservices
Proven experience with LLM orchestration frameworks (e.g., LangChain, LlamaIndex) and deep understanding of vector databases (e.g., Pinecone, Milvus, Weaviate)
Hands-on experience with modern data stacks (Spark, Kafka, Snowflake, Databricks) and cloud environments (AWS, GCP, or Azure)
Expert knowledge of Kubernetes, Docker, and CI/CD patterns specifically for machine learning lifecycles
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