Senior Software Engineer (agentic AI Applications)

Redfin Redfin · Seattle · Detroit, MI

Senior Software Engineer to build and scale an AI-first product for mortgage bankers, focusing on agentic AI applications that analyze client communications and engagement signals to identify high-conversion clients. The role involves full-stack development, owning features end-to-end, and using AI as a core component of the software development lifecycle. It offers the autonomy of a startup with the resources of an established company, with opportunities for technical leadership and setting AI-native engineering standards.

What you'd actually do

  1. Own full-stack features from problem definition through design, implementation, release, measurement, and iteration.
  2. Build agentic AI capabilities such as agent orchestration, tool calling, retrieval-augmented workflows, and LLM-powered features.
  3. Design evaluation, observability, and guardrail mechanisms for AI agents, ensuring reliability, safety, and measurable quality as agent behavior translates into product functionality.
  4. Design and implement scalable backend and frontend solutions using technologies such as C#, Python, Angular, AWS, and Kubernetes.
  5. Partner with product, design, data, and business stakeholders to translate ambiguous business needs into reliable technical solutions.

Skills

Required

  • 5+ years of professional software development experience.
  • Strong experience building production software in languages such as C#, Java, Python, JavaScript, or TypeScript.
  • Experience building full-stack applications across frontend, backend, APIs, and data integrations.
  • Experience designing, deploying, or operating applications on cloud platforms such as AWS, Azure, or Google Cloud.
  • Strong grasp of software design, system architecture, testing, observability, and maintainability.
  • Demonstrated ability to solve ambiguous problems and collaborate effectively across teams.

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.
  • Strong hands-on usage of AI tools in professional software development workflows, such as AI coding agents, AI-assisted reviews, prompt-driven development, or multi-agent workflows.
  • Experience with AWS, Kubernetes, CI/CD, automated testing, and production operations.

What the JD emphasized

  • agentic AI applications
  • AI is not a side tool here
  • core part of how you design, code, test, debug, review, and ship software
  • agent orchestration
  • tool calling
  • retrieval-augmented workflows
  • LLM-powered features
  • evaluation, observability, and guardrail mechanisms for AI agents
  • agentic AI frameworks
  • LLM application patterns
  • agent evaluation harnesses
  • guardrails
  • production monitoring of AI behavior
  • AI tools in professional software development workflows
  • AI coding agents
  • AI-assisted reviews
  • prompt-driven development
  • multi-agent workflows

Other signals

  • AI-first product
  • agentic AI applications
  • core part of how you design, code, test, debug, review, and ship software
  • founding team members