Sr AI Enterprise Engineer

The Trade Desk The Trade Desk · Media · Bellevue, WA · Software Engineering

This role focuses on building and deploying internal AI solutions using LLMs, RAG, and agentic systems within an enterprise context. The engineer will be an end-to-end owner, collaborating with stakeholders to identify opportunities and deliver tools that improve productivity and streamline workflows. Key responsibilities include designing and deploying agentic systems, contributing to RAG applications, building and deploying agents using platforms like Microsoft Copilot and Anthropic Claude, and iterating on conversational agents. The role also involves driving quality through testing and evaluation, and mentoring teammates.

What you'd actually do

  1. Design and deploy intelligent agentic systems that integrate large language models (LLMs) with enterprise data, tools, and workflows using frameworks like LangChain, LlamaIndex, and Semantic Kernel.
  2. Contributring to Retrieval-Augmented Generation (RAG) applications using tools like Azure AI Search, vector databases, and secure enterprise connectors to deliver contextual insights.
  3. Build and deploy agents using Microsoft Copilot, Copilot Studio, Anthropic Claude, and similar platforms to help teams operationalize solutions within enterprise guardrails.
  4. Build and iterate on conversational agents that solve real-world problems, meet stakeholder needs, and deliver measurable business value.
  5. Deliver high-impact features by collaborating across teams, leading through ambiguity, and aligning technical solutions with business goals.

Skills

Required

  • Python
  • AI-powered systems or products
  • data ingestion and transformation
  • APIs
  • ETL pipelines
  • connectors
  • vector databases
  • retrieval strategies
  • communication across technical and non-technical audiences
  • collaboration
  • fast learner
  • complex problem solving

Nice to have

  • C#
  • SQL
  • TypeScript/React
  • Microsoft Copilot
  • Copilot Studio
  • Anthropic Claude
  • LangChain
  • LlamaIndex
  • Semantic Kernel
  • Azure AI Search

What the JD emphasized

  • end-to-end owner
  • AI enablement
  • building agents
  • guiding partners through their AI journey

Other signals

  • building internal AI solutions
  • leveraging large language models
  • retrieval-augmented generation (RAG)
  • agentic systems
  • end-to-end owner