Software Engineering Manager (Agentic AI Applications)

Rocket Companies•Remote - Michigan, MI

1d•$123,500 - $276,000

About The Position

As the Engineering Lead, you will lead the team building a newly launched AI-first product with direct revenue impact. Our platform analyzes client communications and engagement signals to help mortgage bankers, the sales professionals who guide clients through home financing, focus on the clients most likely to convert. Your team builds agentic AI applications: systems where agents reason, plan, use tools, and act on their own to drive real business outcomes. This is a player-coach role. You'll spend real time in the code: shaping architecture, reviewing designs, building critical pieces yourself, and setting the technical bar by example. At the same time, you'll own the health and direction of the team: hiring, growing engineers, setting priorities with product partners, and making sure the team ships things that matter. AI is not a side tool here. You'll use it as a core part of how you and the team design, code, test, debug, review, and ship software. The ideal candidate is a strong engineer who genuinely wants to lead: someone energized by building a team and a product at the same time, and by defining what AI-native engineering looks like in practice. This is a rare setup. You get the autonomy, pace, and greenfield ownership of an early-stage startup, backed by the stability, resources, and built-in distribution of an established company. The product just launched, usage is growing, and both the architecture and the team are still being shaped. You won't inherit someone else's team or someone else's technical decisions. You'll build both, and your choices will define how this product and this team work for years. You'll also get access to frontier AI development tools, including AI coding agents like Claude Code, as a core and funded part of how the team works.

Requirements

8+ years of professional software development experience, including 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 Haves

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.

Responsibilities

Lead a team of engineers building agentic AI capabilities such as agent orchestration, tool calling, retrieval-augmented workflows, and LLM-powered features.
Stay hands-on: contribute to architecture, write and review code, and build critical components alongside the team.
Own the technical strategy for the platform, balancing speed, scalability, maintainability, and business impact.
Set the standard for evaluation, observability, and guardrails for AI agents, ensuring reliability, safety, and measurable quality in production.
Hire, coach, and grow engineers, giving clear feedback and creating real growth paths for the team.
Partner with product, design, data, and business stakeholders to set priorities and translate ambiguous business needs into a clear roadmap.
Define how the team works: engineering practices, AI-native development workflows, code review culture, and delivery cadence.
Lead collaboration across distributed engineering teams, including partners in India.