Senior Software Engineer (Agentic AI Applications)

Rocket Companies•Detroit, MI

About The Position

As a Senior Software Engineer, you will help build and scale 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. You'll build agentic AI applications: systems where agents reason, plan, use tools, and act on their own to drive real business outcomes. This role is for an engineer who wants to own meaningful product outcomes end to end: understanding the business problem, shaping the technical approach, building across the stack, deploying to production, and learning from real usage. AI is not a side tool here. You'll use it as a core part of how you design, code, test, debug, review, and ship software. You'll work closely with engineers, product partners, and business stakeholders across distributed teams. The ideal candidate is highly driven, comfortable with ambiguity, and excited to help define 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 the architecture is still being shaped. Engineers joining now are effectively founding team members whose decisions will define the platform for years. As the product and team grow, so does the opportunity: technical leadership, staff-track growth, and the chance to set the standard for AI-native engineering across the organization. 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

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 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.
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.

Responsibilities

Own full-stack features from problem definition through design, implementation, release, measurement, and iteration.
Build agentic AI capabilities such as agent orchestration, tool calling, retrieval-augmented workflows, and LLM-powered features.
Design evaluation, observability, and guardrail mechanisms for AI agents, ensuring reliability, safety, and measurable quality as agent behavior translates into product functionality.
Design and implement scalable backend and frontend solutions using technologies such as C#, Python, Angular, AWS, and Kubernetes.
Partner with product, design, data, and business stakeholders to translate ambiguous business needs into reliable technical solutions.
Contribute to architecture and technical strategy decisions that balance speed, scalability, maintainability, and business impact.
Collaborate effectively across distributed engineering teams, including partners in India.
Mentor other engineers by sharing patterns, improving team workflows, and modeling high ownership.