**Overall Experience: Positive**
I recently completed the interview process with Sears Home Services for an AI-focused engineering role and had a very positive experience throughout the process.
The process included:
* Initial recruiter conversations
* A take-home technical assignment involving the design and implementation of a Voice AI system
* A systems design interview focused on agentic AI and real-world business workflows
The take-home assignment was practical and relevant to the role. Rather than focusing on algorithmic coding challenges, it evaluated the ability to design and build an end-to-end AI solution involving voice interaction, diagnostics, workflow orchestration, and scheduling. I appreciated that the assignment reflected real business problems rather than abstract exercises.
The systems design interview was one of the more interesting AI-related interviews I have participated in. The discussion focused on designing production-ready agentic systems, workflow automation, orchestration, reliability, tradeoffs, scalability, and human-in-the-loop considerations. The conversation felt collaborative and aligned with real-world challenges rather than purely theoretical AI topics.
What stood out most was the emphasis on practical engineering. The interviewers appeared interested in how candidates think about production systems, business constraints, operational reliability, and AI integration rather than simply testing knowledge of specific frameworks or models.
**Pros**
* Relevant and realistic technical challenges
* Strong focus on practical AI applications
* Professional and responsive recruiting process
* Interesting discussions around agentic AI and business workflows
* Opportunity to demonstrate system design and architecture skills
**Cons**
* The take-home project requires a meaningful time investment
* Expectations around deployment and live testing should be planned for in advance
**Advice for future candidates**
Prepare to discuss architecture, orchestration, reliability, observability, failure handling, and business tradeoffs. Understanding when to use AI versus deterministic workflows is likely more important than discussing model details or prompt engineering techniques.