Staff Enterprise AI Engineer

Peloton Peloton · Consumer · Headquarters, NY · Security & Risk

Staff Enterprise AI Engineer to architect and build the 'Operating System' for deploying AI Agents safely and at scale within Peloton's enterprise tech strategy. This role involves building an Agentic Orchestration Platform, integrating with internal APIs, managing memory stores, enforcing security, deploying EvalOps, defining engineering standards, and optimizing inference costs. It requires a strong software engineering background with experience in MLOps, LLM Orchestration, distributed systems, Python, Go, Kubernetes, and security principles.

What you'd actually do

  1. Architect the "Intelligence & Integration" Layers
  2. Design and build a scalable Agentic Orchestration Platform (using LangChain, LangGraph, or custom frameworks) that allows internal developers to spin up autonomous agents.
  3. Implement the "Integration Layer" ensuring all AI agents connect to internal APIs (Workday, Snowflake, SAP) via secure, standardized protocols (Model Context Protocol - MCP).
  4. Solve the "State Problem" for AI, architecting memory stores (Vector DBs like Pinecone/Weaviate) that persist context across user sessions.
  5. Enforce "Security by Design"

Skills

Required

  • Python (production grade)
  • Kubernetes (EKS)
  • Docker
  • Infrastructure-as-Code (Terraform)
  • OAuth 2.0 (OBO flow)
  • RBAC
  • zero-trust networking principles
  • LangChain
  • LangGraph
  • Pinecone
  • Weaviate
  • MLOps
  • LLM Orchestration
  • Large Scale Distributed Systems

Nice to have

  • Go
  • Model Context Protocol (MCP)
  • FinOps
  • HIPAA
  • SOX

What the JD emphasized

  • Founding Engineer
  • Operating System
  • AI Agents
  • Player/Coach
  • Golden Path
  • Intelligence & Integration
  • Agentic Orchestration Platform
  • Integration Layer
  • State Problem
  • Security by Design
  • Data Clean Rooms
  • EvalOps pipelines
  • Engineering Standards
  • Vibe Code
  • guardrails
  • Capital-Efficient Scale
  • inference costs
  • Semantic Caching
  • Kubernetes (EKS)
  • distributed systems problem
  • latency
  • rate limiting
  • eventual consistency
  • prompt engineering
  • tools that solve specific business frictions
  • mentoring senior engineers
  • demystifying AI

Other signals

  • Architecting and building an Enterprise AI Platform
  • Deploying AI Agents safely and at scale
  • Building the 'Golden Path' for AI leverage
  • Focus on Infrastructure, Security, and Orchestration