Senior Director, AI Engineering Transformation

Sr. Director, AI Engineering Transformation

Location:

Boston, MA preferred;
New York, NY acceptable (On‑site)

Reports to:

VP, Chief of Staff to the CEO

Dotted Line:
Chief Technology Officer / Chief Engineering Officer

Direct Reports: AI Transformation Team (AI Fellows)

This is a high‑impact leadership role at the intersection of AI, engineering, product quality, and organizational transformation. You will own the strategy and execution of AI integration across Shark Ninja’s Engineering organization, spanning the full development lifecycle from concept and architecture through design, validation, and production, and extending into reliability engineering, DFM/DFT, test data, and manufacturing engineering. You will build the data foundation, deploy AI capabilities, and transform workflows so engineers develop products faster, smarter, and with higher quality at every stage.

Data Foundation & Infrastructure

• Architect and implement a unified data infrastructure across Engineering, resolving the fragmented, siloed data landscape that is the #1 bottleneck to AI adoption.
• Map data assets across the organization (schematics, CAD/PCB design files, test results, FMEAs, DVT/EMC results, lessons learned, field return data, quality metrics) and build ingestion pipelines that make this data usable by AI systems.
• Partner with IT and data engineering to connect Engineering’s data infrastructure to the enterprise data architecture (Snowflake, AWS).

Engineering Transformation

• Define and execute the AI transformation roadmap across the full engineering lifecycle, while continuously identifying new AI opportunities with Engineering leadership as capabilities evolve.
• Lead the reimagination of program management infrastructure, replacing manual, PowerPoint‑based workflows with AI‑powered tooling that enables real‑time program status visibility, automated accountability, and permission‑based dashboards.
• Deploy AI‑powered planning intelligence by ingesting historical engineering data to improve forecasting accuracy and reduce late‑stage surprises.
• Transform reliability and quality capabilities, failure prediction, DVT/EMC outcome analysis, field‑return pattern detection at scale, and partner with Engineering leadership to define the next generation of AI‑powered engineering quality.
• Reimagine design and test workflows by making lessons learned, test results, and FMEAs accessible through AI, so past engineering knowledge automatically informs future product development.
• Strengthen Engineering’s contribution to product requirements by building AI systems that surface relevant historical data (prior test failures, field issues, quality patterns) during the design and requirements process.

Team Building & Change Management

• Build, lead, and scale a team of AI Fellows embedded directly into Engineering teams to drive hands‑on AI adoption.
• Drive change management across large, global engineering teams with varying levels of AI fluency, focusing on director‑level and below adoption.
• Translate complex AI capabilities into practical, adoptable solutions that engineers, program managers, and cross‑functional teams actually use.
• Establish success metrics, track adoption, and report measurable outcomes to executive leadership.
• Champion a culture of experimentation: fast iteration, learning from failure, and scaling what works.

• 10+ years of professional experience in AI/ML, engineering leadership, program management, data architecture, quality engineering, or technology transformation roles.
• Strong technical fluency in AI/ML and data infrastructure: evaluate tools, assess platforms, understand data pipelines, and engage in technical discussions with engineers, data scientists, and product teams.
• Proven track record leading large‑scale transformation or change management initiatives, ideally in engineering, R&D, or consumer products environments.
• Deep understanding of engineering development life cycles in a hardware or consumer electronics context, including stage‑gate processes, DVT/EVT/PVT builds, and cross‑functional launch execution.
• Proven ability to both build (hands‑on implementation) and think strategically.
• Strong…