Okay, I've searched for Staff Machine Learning Engineer roles in Lehi, UT, requiring 5+ years of experience, full-time positions, with skills in LLM fine-tuning and MLOps. I have found 23 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

This Staff Machine Learning Engineer role is fundamentally about architecting and refining AI systems that can reliably extract and standardize complex data from a variety of structured and unstructured sources. Success hinges on mastery of multi-agent AI systems—meaning this engineer must design and optimize how numerous specialized AI agents interact seamlessly and intelligently to enhance accuracy and efficiency. The role demands deep expertise in state-of-the-art tools and techniques such as large language model (LLM) fine-tuning, prompt engineering, and cutting-edge protocols like agent2agent (A2A) and Model Context Protocol (MCP), reflecting the sophisticated nature of the AI workflows at play. Beyond core ML engineering skills, there is a clear emphasis on operationalizing these models through MLOps best practices—including deployment, monitoring, automated evaluation, and continuous retraining—pointing to a role that balances research-level experimentation with enterprise-grade production reliability. Additionally, the engineering challenges involve optimizing performance metrics like latency and cost, managing complex data extraction pipelines, and integrating these AI solutions tightly with broader data engineering and product teams. Autonomy and cross-disciplinary collaboration are implicit expectations since this role requires navigating ambiguity in multi-agent coordination, driving innovation in workflow automation, and ensuring model explainability in production. Early success would likely be measured by demonstrable improvements in data extraction accuracy, system scalability, operational stability, and the seamless delivery of AI-powered insights within key business pipelines.

Company Analysis:

Smith Johnson Tech (SJT) is a well-established, employee-owned IT staffing and recruiting firm with deep roots and extensive experience in the Western U.S. and Hawaii markets. While the company itself is not a traditional tech product firm, its role as an IT staffing leader means it operates at the nexus of cutting-edge technology demands and talent solutions. This positioning implies the company values individuals who can deliver high-quality, reliable expertise and thrive in client-facing, solution-oriented environments. The culture at SJT likely prioritizes professionalism, continuous learning, and collaboration, given their emphasis on a positive candidate experience and their detailed recruiting processes. Since this job is for a client of SJT rather than SJT itself, understanding this staffing context is key: the role will likely be embedded in a dynamic, fast-evolving AI business operating out of Lehi, Utah, emphasizing innovation and technical depth in machine learning. For candidates, this means thriving requires agility, a proactive mindset for on-site collaboration, and the ability to integrate within multi-disciplinary teams. The company’s commitment to personalized service and quality talent placement also suggests a high standard for professionalism and owning outcomes. Strategically, the role aligns with scaling AI capabilities using advanced multi-agent architectures, signaling a growth-driven, innovation-focused client where technical leadership and hands-on expertise in emerging AI methodologies are paramount.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you had to optimize the interaction between multiple AI agents or components. What challenges did you face and how did you resolve them?
• Behavioral: Describe a situation where you identified performance bottlenecks in an AI or ML system. How did you diagnose and improve the system?
• Behavioral: Give an example of how you've implemented feedback loops or continuous learning mechanisms in an AI model deployment environment.
• Behavioral: Can you provide an example of collaborating closely with data engineers and product teams to integrate an ML solution into business workflows? What was your approach?
• Technical: How would you approach fine-tuning a large language model to improve extraction accuracy for both structured and unstructured data sources?
• Technical: Explain your experience with multi-agent AI architectures. How do you design systems to ensure reliable and efficient interactions between agents?
• Technical: What strategies and tools have you used for MLOps, particularly in monitoring and automated retraining of deployed models?
• Technical: Discuss how you ensure model explainability and reliability in AI systems operating in production environments.
• Situational: Imagine the multi-agent system you designed starts producing inconsistent extraction results after a recent model update. How would you troubleshoot and resolve this?
• Situational: You are tasked with improving latency and cost-efficiency of a large-scale AI extraction pipeline. What optimization methods would you consider?
• Situational: How would you prioritize improvements if you received conflicting feedback from product teams and data engineers regarding the AI model’s performance?
• Company Fit/Motivation: Why are you interested in working on AI-driven data extraction systems in a client environment such as this one?
• Company Fit/Motivation: How do your professional values and working style align with a company that emphasizes collaboration, continuous learning, and operational excellence?
• Company Fit/Motivation: Given that this is an on-site role in Lehi, Utah, how do you plan to ensure effective communication and integration with cross-functional teams?
• Questions for Interviewer: Can you describe the current team dynamic and how the Staff Machine Learning Engineer collaborates day-to-day with ML researchers and data engineers?
• Questions for Interviewer: What are the biggest technical challenges the AI extraction systems are currently facing, and what would you consider a meaningful impact from this role within the first 6 months?
• Questions for Interviewer: How does the company support continuous learning and professional development specifically for AI engineers working on cutting-edge technologies?