As Senior Director of Control Systems Engineering, I lead two AI initiatives at SharkNinja: standardizing LLM-assisted workflows across the engineering team, and building an internal AI diagnostic tool that lets non-engineers reason about product behavior directly from unit telemetry.

Impact

• Same engineering headcount supports approximately 40% more concurrent projects
• Non-engineers now triage software and hardware issues from unit CSV data without controls-engineering time
• Shorter idea-to-prototype cycle time on new controls features
• Higher documentation coverage and design-decision traceability through MCP-integrated Confluence workflows

AI-assisted engineering — team adoption

Standardized how the Control Systems team uses Claude Code and OpenAI Codex in daily work. Rolled out through power-user pilots and shared prompt patterns rather than top-down mandate.

Engineering workflows

• Code generation — controller scaffolding, sensor-driver boilerplate, test-harness code, and embedded-system utilities
• R&D and ideation — algorithm exploration, alternative approaches to controls problems, tradeoff analysis
• Data-analysis pipelines — plotting, statistical analysis, and exploration of experimental sensor data
• Code comparison and change-log automation — diff summarization, PR review assistance, and release-note generation

Documentation and knowledge capture

• Confluence via MCP integration — design documents, meeting notes, and technical specs written, updated, and cross-linked directly in Confluence
• Documentation kept current at a granularity the team could not sustain manually
• Design decisions captured with the reasoning behind them, not only the final outcome

Internal AI-assisted product diagnostics

Led development of an internal tool that gives non-engineers — product managers, QA, and customer-support engineers — direct access to product-specific reasoning without going through controls engineering.

Capabilities

• Natural-language questions about how a feature works, what a control loop does, or why a given behavior might occur
• CSV telemetry analysis — upload unit data and get an automated first-pass diagnosis of sensor anomalies, thermal or fluid-behavior deviations, control-loop faults, and edge-case triggers
• Grounded in team knowledge — the tool draws on internal product documentation, historical failure modes, and engineering context, so answers are product-specific rather than generic LLM output

Effect on the team

• Product managers, QA, and customer-support teams self-serve the first layer of technical questions
• Controls engineers spend less time on repeated first-line diagnostics
• Issues found in the field or in QA can be triaged in hours instead of days