Staff Enterprise AI Engineer
Directly mentions enabling "Vibe Code" (low-code/assisted coding) and focuses on building AI dev/platform tools and agent orchestration (LangChain, EvalOps, vector DBs).
About the Role
Peloton is hiring a Staff Enterprise AI Engineer to be the founding engineer for an Enterprise AI Platform, architecting infrastructure, security, and orchestration to enable safe, scalable deployment of AI agents across the company. The role is a player/coach position: build the core platform (infrastructure, memory stores, integrations, eval pipelines) while mentoring engineers and defining standards for safe, cost-efficient AI usage.
Job Description
Role
Peloton seeks a Staff Enterprise AI Engineer to architect and build an Enterprise AI Platform that enables Product, People, and Operations teams to deploy AI agents safely and at scale. This is a platform/engineering role (not a research or hyperparameter-tuning data science role) focused on infrastructure, security, orchestration, and operationalizing agentic workflows.
Key Responsibilities
- Design and build a scalable agent orchestration platform (LangChain/LangGraph or custom) to allow internal developers to create autonomous agents.
- Implement integration layers so agents can securely connect to internal APIs and systems (Workday, Snowflake, SAP) using standardized protocols (Model Context Protocol - MCP).
- Architect persistent state and memory stores (vector DBs such as Pinecone/Weaviate) to maintain context across sessions.
- Partner with security leadership to implement Identity Propagation and ensure agents execute with correct OAuth scopes to prevent privilege escalation.
- Build data clean rooms and PII-masking pipelines to prevent leakage of sensitive data to model providers.
- Deploy EvalOps pipelines to test models for hallucination and regression before production rollout.
- Define engineering standards, templates, and libraries to enable low-code/assisted “Vibe Code” workflows within guardrails.
- Perform rigorous code reviews emphasizing high performance, low latency (<200ms) and cost efficiency; optimize inference costs via semantic caching and routing to appropriate models.
- Manage ephemeral AI compute using Kubernetes (EKS) and other platform tooling.
- Mentor senior engineers and translate technical trade-offs for non-technical stakeholders.
Requirements
- 10+ years of software engineering experience, with 3+ years focused on MLOps, LLM orchestration, or large-scale distributed systems.
- Deep fluency in production-grade Python; Go is preferred for platform services.
- Proven experience deploying RAG (Retrieval Augmented Generation) and agentic workflows in production; familiarity with frameworks like LangChain or Semantic Kernel.
- Strong platform engineering experience with Kubernetes (EKS), Docker, and Infrastructure-as-Code (Terraform).
- Solid understanding of OAuth 2.0 (including OBO flow), RBAC, and zero-trust networking principles.
- Experience designing for latency, rate limiting, consistency, and inference cost optimization.
- Strong communication skills to explain technical trade-offs to executive stakeholders.
Bonus
- Experience implementing Model Context Protocol (MCP) or similar standardized tool interfaces.
- Background in FinOps (managing GPU/cloud spend).
- Experience operating in highly regulated environments (HIPAA, SOX, etc.).