I'm a technical product manager specializing in AI infrastructure, ML platforms, and developer tools. With experience at Google Cloud and Amazon, I've driven multi-million dollar technical initiatives, shipped production ML systems, and enabled enterprise customers to scale their AI/ML workloads.
My background combines deep technical expertise in cloud infrastructure, machine learning operations, and data platforms with strong product instincts. I excel at translating complex technical requirements into product strategy, working cross-functionally with engineering and data science teams, and shipping products that customers love.
I recently completed my MBA at Wharton with a focus on AI/ML product strategy. I'm passionate about building the next generation of AI infrastructure and developer experience tools.
How I approach product management, shaped by years of building technical infrastructure products and enabling customer success.
Don't just write specs—get hands-on with the technology. Whether it's debugging a Kubernetes deployment or testing API integrations, I believe PMs should deeply understand what they're building. This builds credibility with engineering teams and customers.
The best PMs are creative problem solvers who find novel solutions when standard approaches fail. When Wayfair's $26M SQL Server migration was blocked, I identified a partner solution nobody else knew about. Always ask: what's the unconventional path forward?
Achieve trusted advisor status quickly by demonstrating deep customer empathy and technical expertise. This means doing your homework, understanding their business context, and bringing solutions to problems they haven't articulated yet.
Complex technical products require alignment across engineering, sales, partnerships, and customers. I focus on bringing the right resources together, facilitating rather than dictating, and ensuring everyone understands the "why" behind decisions.
Perfect is the enemy of shipped. Focus on getting working solutions in customers' hands, gathering feedback, and iterating. The Wayfair immersion days weren't perfect, but they unblocked millions in opportunity and built momentum.
Technical excellence without business outcomes is just engineering. Business strategy without technical feasibility is just PowerPoint. Great product managers operate at the intersection—understanding both the architectural implications and the revenue impact.
Led product strategy and execution for an AI-powered Risk Index platform, integrating ML models with behavioral economics principles to improve financial risk assessment.
Led the technical migration of Wayfair's ML infrastructure to Kubeflow-based Vertex AI pipelines, enabling their Supply Chain Science and MarTech teams to accelerate model development and deployment.
Architected technical product strategy for integrating smart locks into the Ring ecosystem, defining API requirements, edge computing needs, and IoT infrastructure.
Led comprehensive infrastructure analysis and optimization for Home Depot's data processing workloads, implementing alternative architectures that dramatically reduced costs.
Personal side projects and academic work exploring AI infrastructure, ML operations, and developer tools. These are ongoing experiments and learning exercises.
Designed and implemented a system of four specialized AI agents for automated startup evaluation at venture capital firms. Agents collaborate to assess product-market fit, market size, competitive landscape, and founding team strength using prompt engineering and external data sources.
Building a tool to benchmark and compare inference costs across different LLM providers (OpenAI, Anthropic, Together AI, Replicate) for various use cases. Helps teams make data-driven decisions about which provider/model to use based on their specific workload patterns.
Framework for testing and comparing different RAG (Retrieval-Augmented Generation) architectures. Evaluates various embedding models, vector databases, and retrieval strategies to identify optimal configurations for different document types and query patterns.
Lightweight monitoring dashboard for tracking ML model performance in production. Focuses on drift detection, latency monitoring, and cost tracking - designed for small teams without dedicated MLOps infrastructure.
Experimenting with a Git-like system for managing prompt engineering workflows. Enables teams to version prompts, compare outputs, and track performance metrics across iterations - addressing a gap in current AI development tools.
Excerpts from peer reviews and manager feedback during my time at Google Cloud.
"Najneen was individually responsible for unblocking a significant $26M ARR database workload by identifying a novel ISV-based approach which not only resulted in unblocking of the migration but also $4M in ISV spend. She is trusted by her teammates and customer stakeholders to not only lead significant workload sales cycles but also to be a thought leader and evangelist for GCP."
— Charles Asik, Head of Customer Engineering, Google Cloud
"Najneen is a leader and a natural problem solver. She stepped into a leadership role on the account team and drove us towards a solution. She is extremely well-organized, skillful at stakeholder management, and very effective at gaining customer trust by 'leading from the front'. Not only is she a creative problem-solver, she is tenacious and effective in seeing the problem through to solution on-the-ground."
— Josh Davis, Solutions Consultant, Google Cloud
"Najneen does an astonishing work when working with cross-functional teams. I've seen her engaging with TAMs, other CEs, and PMs to come up with the best solution for the customer. She is always willing to help and takes the lead on several initiatives where she looks for nothing but contributing the most she can for the customer."
— Gilberto Gutierrez, Customer Engineer, Google Cloud
"She bypassed most of her 'Noogler' period and jumped immediately into high impact account work. Despite only recently joining the Wayfair team, Najneen has made her presence felt through her contributions to unblocking a major migration of SQL Server. She is recognized by her peers as a versatile technologist, at home both leading technical evaluations and facilitating training sessions."
— David Bryan, Manager, Sales Engineering, Google Cloud
Google Cloud blog post detailing how Wayfair leveraged Vertex AI and MLOps practices to optimize their supply chain operations through machine learning pipelines.
Wayfair's tech blog highlighting their cloud transformation journey and recognition as Google Cloud's 2023 Retail Industry Customer of the Year.
Press release on the AI-powered Risk Index platform developed at Wharton's Choice Architecture Lab, applying behavioral economics and ML to regulatory compliance.
Additional technical writing samples and thought leadership available on LinkedIn