Scout Motors
AI Agent Engineer
About this role
Scout Motors seeks an AI Agent Engineer to design, develop, and deploy intelligent agents on Databricks that support quality and operational insights across the organization. You'll own the complete lifecycle from prototyping to production, build evaluation frameworks using MLflow, and collaborate across teams to continuously improve agent performance using real-world vehicle and manufacturing data.
What you'll do
- Design, develop, and deploy AI agents on Databricks from prototype through production, managing versioning, monitoring, and maintenance
- Build and maintain evaluation frameworks using MLflow to assess agent quality across correctness, safety, and business criteria
- Establish offline evaluation pipelines with curated datasets and benchmarks, plus online production monitoring with MLflow tracing
- Integrate human feedback from engineering, quality, and business teams into agent improvement workflows
- Collaborate with Data Engineers and R&D to define data pipelines and configurations that reliably feed agents
- Create dashboards and reports translating agent outputs into actionable insights for business stakeholders
What they're looking for
- AI agent development and design patterns
- Databricks platform and ecosystem
- MLflow evaluation suite and tracing
- Python and software engineering practices
- Data pipeline design and management
- Machine learning evaluation and monitoring
- Cross-functional collaboration and communication
- Data quality and validation
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Scout Motors
Scout Motors develops next-generation electric and extended-range electric pickup trucks and SUVs with authentic off-road performance. The company is hiring engineers across powertrains, body structures, electronics, and audio systems to design and integrate these vehicles from concept through production.
- Website
- scoutmotors.com
Likely interview questions
- Describe your experience designing and deploying AI agents in production environments—what challenges did you face with versioning and monitoring?
- How have you approached building evaluation frameworks for AI systems? What metrics or judges did you implement?