Engineering Product Manager

Cisco•Milpitas, CA

2d•$168,800 - $277,400•Hybrid

About The Position

The Cisco AI Software & Platform Group incubates and delivers Generative AI based solutions to improve Cisco's existing Products. Our Group is also introducing new offerings that help customers roll out Generative AI at scale while doing so responsibly. As an Engineering Product Manager for AI Canvas Growth, you will be instrumental in improving AI Canvas quality and customer outcomes. Operating with startup energy and ownership, you will define product strategy, roadmap, and execution by grounding decisions in data from usage telemetry, product analytics platforms such as Amplitude, and evaluation frameworks. You will bring the rigor of management consulting and the velocity of a high-growth startup to drive measurable improvements in quality and adoption (not hands-on model training).

Requirements

8+ years of experience in product management, with at least 5 years in AI-driven or data-intensive software products.
Bachelor's degree or equivalent experience in Computer Science, Engineering, or related technical field.
Experience with Generative Artificial Intelligence and Large Language Model-enabled products, ideally within a networking or enterprise technology context.
Strong proficiency in Structured Query Language and Python for data analysis.
Hands-on experience with product analytics tools such as Amplitude (or similar) and evaluation frameworks for quality measurement.
Experience at a premier management consulting firm (McKinsey, BCG, Bain) defining, launching, and scaling successful software products by using data to improve quality and drive adoption at scale, demonstrating significant business impact in an enterprise environment.
Experience using modern development and AI-assisted tools (Cursor, Vercel) to accelerate product velocity.

Nice To Haves

Master's degree, Doctor of Philosophy or equivalent experience, or Master of Business Administration in Computer Science, Artificial Intelligence, Machine Learning, or a related business or quantitative field.
Deep understanding of data pipelines and analytics infrastructure, and how to use them to drive product decisions.
Startup energy and attitude, including high ownership, comfort with ambiguity, bias to action, and ability to thrive in fast-paced environments.
Strategic understanding of Artificial Intelligence platform architectures and machine learning operations principles, with experience integrating sophisticated Artificial Intelligence capabilities into existing enterprise software, especially within the networking domain.
Proven ability to articulate technical and product strategies to diverse audiences, including executive leadership, technical teams, and external customers, driving alignment and execution.
Proven experience using data, evaluation signals, and customer feedback to improve product quality and drive product-led growth and adoption in business-to-business or enterprise software-as-a-service contexts.

Responsibilities

Define and implement the strategy and roadmap adoption, using usage data, evaluation signals, and telemetry to drive priorities for the entire team.
Collaborate deeply with research, data science, and engineering teams to translate technical capabilities into product improvements that drive growth and adoption, leading the product lifecycle from concept to launch based on measured quality and user impact.
Identify and validate high-impact opportunities through data-driven analysis of user behavior, product analytics, evaluation results, and customer feedback, translating quantitative insights into prioritized product requirements and roadmap decisions.
Partner with cross-functional teams including engineering, user experience, sales, and marketing to ensure successful delivery, market adoption, and continuous iteration of AI Canvas, employing modern development tools (Cursor, Vercel) to accelerate execution velocity and shorten feedback loops.
Define, track, and report on key product metrics including growth, adoption, retention, engagement, and quality. Use product analytics platforms such as Amplitude, evaluation frameworks, and telemetry pipelines to inform data-driven decisions, run thorough experiments, and optimize performance while encouraging responsible AI practices, ethical considerations, and data privacy.