ABOUT THE ROLE
Peloton is looking to transform our enterprise tech strategy with AI adoption. We are looking for a Senior Enterprise AI Engineer to serve as the "Founding Engineer" of our Enterprise AI Platform.This is not a traditional Data Science role. You will not spend your days tweaking hyperparameters. Instead, you will architect and build the Operating System that enables our Product, People, and Operations teams to deploy AI Agents safely and at scale. You will act as a "Player/Coach," laying the technical foundation (Infrastructure, Security, Orchestration) while guiding a team of engineers to execute the vision. You will build the "Golden Path" that helps everyone at Peloton to leverage AI securely for the competitive advantage of Peloton.
YOUR DAILY IMPACT AT PELOTON
- Architect the "Intelligence & Integration" Layers
- Design and build a scalable Agentic Orchestration Platform (using LangChain, LangGraph, or custom frameworks) that allows internal developers to spin up autonomous agents.
- Implement the "Integration Layer" ensuring all AI agents connect to internal APIs (Workday, Snowflake, SAP) via secure, standardized protocols (Model Context Protocol - MCP).
- Solve the "State Problem" for AI, architecting memory stores (Vector DBs like Pinecone/Weaviate) that persist context across user sessions.
- Enforce "Security by Design"
- Partner with Security leadership to implement Identity Propagation. Ensure agents execute tasks using the user’s specific OAuth scopes, preventing privilege escalation.
- Build "Data Clean Rooms" and PII masking pipelines to ensure sensitive member or employee data is never leaked to model providers.
- Deploy EvalOps pipelines to automatically test models for hallucination and regression before they hit production
- Define the Engineering Standards
- Define the "Guide vs. Control" standards for the organization. Create the templates and libraries that allow analysts to "Vibe Code" (low-code/assisted coding) safely within our guardrails.
- Perform rigorous code reviews for partner teams and vendors, ensuring high performance, low latency (<200ms), and cost efficiency
- Capital-Efficient Scale
- Optimization of inference costs by implementing Semantic Caching and routing logic (e.g., routing simple queries to smaller/cheaper models).
- Leverage Kubernetes (EKS) to manage ephemeral compute resources for AI workloads.
- A Systems Builder: You view AI as a distributed systems problem. You care about latency, rate limiting, and eventual consistency just as much as you care about prompt engineering.
- A Pragmatist: You don't build "Science Projects." You build tools that solve specific business frictions (e.g., automating Content PR approvals or speeding up Supply Chain queries).