Software Engineering Manager (agentic AI Applications)

Redfin Redfin · Seattle · MI · Remote

Engineering Manager to lead a team building agentic AI applications for a fintech product, focusing on client communication analysis to help mortgage bankers. The role is player-coach, involving hands-on coding, architecture, team leadership, hiring, and setting technical strategy for AI-native development.

What you'd actually do

  1. Lead a team of engineers building agentic AI capabilities such as agent orchestration, tool calling, retrieval-augmented workflows, and LLM-powered features.
  2. Stay hands-on: contribute to architecture, write and review code, and build critical components alongside the team.
  3. Own the technical strategy for the platform, balancing speed, scalability, maintainability, and business impact.
  4. Set the standard for evaluation, observability, and guardrails for AI agents, ensuring reliability, safety, and measurable quality in production.
  5. Hire, coach, and grow engineers, giving clear feedback and creating real growth paths for the team.

Skills

Required

  • 8+ years of professional software development experience
  • 2+ years leading engineers as a manager or tech lead
  • Strong hands-on experience building production software in languages such as C#, Java, Python, JavaScript, or TypeScript
  • Experience designing and operating full-stack systems across frontend, backend, APIs, and data integrations on cloud platforms such as AWS, Azure, or Google Cloud
  • Track record of hiring, coaching, and growing engineers
  • Experience owning technical strategy or architecture for a product or platform
  • Demonstrated ability to work with product and business stakeholders to set priorities and deliver outcomes

Nice to have

  • Hands-on experience building with agentic AI frameworks (e.g., LangGraph, Semantic Kernel, AutoGen, CrewAI, or similar), LLM APIs, or Model Context Protocol (MCP) tool integrations
  • Experience with LLM application patterns such as retrieval-augmented generation (RAG), agent evaluation harnesses, guardrails, or production monitoring of AI behavior
  • Experience leading a team through AI-native ways of working, such as AI coding agents, AI-assisted reviews, or multi-agent workflows
  • Experience building or leading a team in a greenfield or newly formed product environment
  • Experience with AWS, Kubernetes, CI/CD, automated testing, and production operations

What the JD emphasized

  • AI-first product
  • agentic AI applications
  • agent orchestration
  • tool calling
  • retrieval-augmented workflows
  • LLM-powered features
  • agent evaluation harnesses
  • guardrails
  • production monitoring of AI behavior
  • AI-native ways of working

Other signals

  • AI-first product
  • agentic AI applications
  • lead a team building AI capabilities