My path into technology started with a simple act of curiosity: helping a high school friend set up her first Apple computer, which meant teaching myself BASIC along the way. That early spark earned me a full-ride scholarship in Computer Science and eventually carried me to the heart of Silicon Valley where I worked for big tech companies like [Google](company:1) who sponsored my completion of the [Stanford LEAD](https://grow.stanford.edu/browse/stanford-lead-online-business-program) executive education program.
I've led global enterprise deployments spanning Japan, Taiwan, Bahrain, Philippines, Europe and India. Working across such different cultures taught me as much about people as it did about technology, and shaped how I think about collaboration to this day. Alongside that career, I built a life with my husband and raised a [daughter](https://carissaott.com) who is now forging her own path in software engineering. Our Samoyed dog, [Mochi Pancake](https://youtu.be/NzH5PaEgjOs), inspired the creation of Mochi AI chatbot on this website. Feel free to ask Mochi questions about me by clicking on his icon on the lower right-hand corner. In my youth I played Pac-Man in the arcade; try out [Mochi Pac-Man](/pacman) to play this classic video game with Mochi.
Today, I focus on ##leading technology transformations that put people at the center of progress.## I help companies put AI to work at scale, while investing just as much in the growth of the teams behind that innovation.
Driving product technology strategy and agentic AI deployments, building advanced platforms (GrowthOS, Lead Generator, Brand Assessment) that automate enterprise workflows with human-in-the-loop oversight.
Organized unstructured workflows by building GrowthOS as an integrated platform that generates marketing campaign ideas, AI search engine optimization (SEO), lead generation. Improved payroll forecasting using dashboards with data from various sources. Leading hands-on AI upskilling sprints focused on practical automation.
Spearheading the design and deployment of agentic AI transformation frameworks and advising on ethical policy considerations for emerging AI solutions, including Lead Generator (AI finds leads based on ideal client profile) and Brand Assessment (AI measures brand score and provides recommendations for improvement).
Pioneered Grex which uses AI for companies to post problems that can be solved by workers, which can potentially become investable opportunities. Defining partnerships for independent workers to obtain health insurance.
Architecting the AI framework for Just Ride to unify cycling race data with automated pro intelligence and a single source of truth for the global peloton.
Led large-scale engineering operations and full lifecycle program management within Google Engineering, delivering crucial features for Google Maps and leading massive infrastructure and SDLC transformations.
Managed full product lifecycle from requirements to production for 50+ Google Maps features with Gemini Voice Navigation on Google Cloud Platform for 2 billion+ users. Orchestrated PM, UX, Privacy/Security, GeoQuality reviews for Google Maps Content RLHF utilizing Google Search data. Facilitated GPU/TPU requests on GCP when needed.
Led cross-functional execution across engineering, product, QA, and release stakeholders to deliver scaled launches on time.
Championed rapid prototyping and AI evaluation workshops for 30+ TPMs, building fluency in AI-driven program management. Provided consultation on leveraging Google AI tools for SDLC governance.
Built TPM culture centered on technical rigor, mentoring, and execution, contributing to the promotion of senior TPM talent.
Drove AI Service Desk transformation, migrating ticket routing workflows contributing to $150M in organization-wide efficiencies.
Led transformation of Finance SDLC governance for SAP on Google Cloud Platform, providing consultation service for 40+ TPMs; improved time-to-market to 90+%, compliance to 82+%, and reduced defects by 21,000+.
Established program review cadences for a portfolio of 20+ programs, driving cross-functional executive visibility into status, risks, dependencies, and prioritization decisions.
Led infrastructure programs with 50+ TPMs at scale: SAP entity automation with 98% SLO, 67% testing cost reduction via server consolidation, and global IT standardization for vendor offices that reduced millions in operational cost.
Pioneered portfolio management for Finance Engineering using a single source of truth that enabled dashboard reporting for managing projects/programs and identifying risks that need leadership attention.
Led cross-functional teams in implementing lightweight SDLC which improves compliance, timeliness, quality and traceability of Finance system releases.
Built dashboards to monitor monthly releases, building AI/ML solutions for efficiency.
Led 12 cross-functional teams in completing configurations and dashboards for 100 new entities with 304 subledger requests. Improved closure rate to 96%, accelerated burn rate by 65%.
Established Stanford LEAD @ Google to challenge employees to change lives, change organizations, change the world. Negotiated with Stanford GSB, leveraged Google Educational Reimbursement to provide employees up to 72% savings after reimbursement. Participants improved leadership skills which resulted in some promotions.
Improved HR Engineering intake closure to 96%, burn rate 84% with simple intake/backlog management process for 200+ customers from 91 product areas. Automated intake ticket generation, built dashboards to improve service levels.
Built Return to Office dashboard and led full cycle enhancement of Staffing Requests, internal and external job sites to show remote work locations.
Led cross-functional teams in retrofitting 126 HR systems for the Oracle to SAP chart of accounts migration in May 2021.
Led cross-functional teams in implementing integrations for Workday, SAP and homegrown payroll systems in Ireland, Poland, Singapore.
Led cross-functional team from CorpEng, People Operations, Legal, and Information Security to ensure that HR systems are compliant to General Data Protection Regulation (EU GDPR).
Managed software releases for Google's HR integration platform, Workday Payroll integrations and HR Ops API.
Led a cross-functional team from Corporate Engineering, Extended Workforce Solutions, Finance, Legal, and Product Areas to implement a new vendor management system from inception through design. The mission was to drive value, reduce risk, and make it simpler to manage contingent workers.
Delivered XWM Business Intelligence to provide a single source of truth for managing cost, risk, and operational efficiency in contingent workforce engagements.
Brought together subject matter experts from REWS, xWS, NetOps, AV Eng, Vendor Solutions, Physical Security, and Finance to standardize Google Owned Vendor Offices (GOVO) and efficiently build them at a lower cost than Googler offices. Streamlined GOVO operations by creating a guide for GOVO Site Managers to manage Temp/Vendor/Contractor (TVC) migrations and have Equipment Maintenance Technicians as the "smart hands onsite". This saved thousands of dollars in travel cost for Vendor Solutions as it allowed escalations to be handled remotely.
Spearheaded cross-functional collaboration among BizApps, REWS, xWS, PeopleOps, Vendor Solutions, Googler Experience, SecOps, Compliance, and FieldTechs to define the method for tracking information about facilities where TVCs work. This made it possible to verify if TVC facilities follow Google's Vendor Site Checklist with adherence to the User Data Access Policy.
Conceptualized PSH+ to automatically determine TVC access by job function and upload application list into Lantern for provisioning, thus reducing manual work for managers.
Google Cloud PlatformGoogle MapsGemini AISDLC GovernanceSAP IntegrationProgram Portfolio Management
Technical Project Manager
Apple • Full-time
Jun 2009 – Jun 2011
Managed critical development and cross-functional deployment of international recruitment and HR systems within Apple's IS&T division.
Led the software development and launch of Apple HR recruiting systems, including the Apple Job Search user interface.
Successfully deployed application experiences localized and active in 80+ countries.
Collaborated closely with cross-functional design, security, and infrastructure engineering teams.
Directed software implementations, built core transactional systems, and provided expert engineering consultancy across enterprise leaders including Sun Microsystems, eBay, American Express, IBM, DHL, and Infogain.
Sun Microsystems / Sun Java Center: Led the implementation of HP Project & Portfolio Management (PPM) software to optimize outsourcing workflows, and served as senior consultant architecting Java EE solutions for eBay, American Express, and CBOE.
Infogain Consultant: Led the full-cycle development of specialized Data Transfer Systems and Loan Collection Systems.
DHL: Co-developed the global Shipment Control System for real-time parcel and logistics tracking.
IBM Consultant: Led critical technical enhancements of TECSYS Financials & Distribution Systems for international clients.
Head of Technology Transformation / Architect • Work in progress
A cutting-edge agentic platform that automates critical business operations, generating marketing campaign ideas, orchestrating AI-driven SEO, and facilitating lead generation with a unified manager cockpit.
Engineered an integrated dashboard that centralizes multiple agentic workflows.
Optimized payroll forecasting using dynamic dashboards pulling data from diverse cloud and internal sources.
Pioneered human-in-the-loop agency models ensuring guardrails, safety, and transparency.
An AI-powered sales-intelligence agent that automatically finds, analyzes, and scores target prospects based on a company's ideal client profile (ICP).
Implemented intelligent matching algorithms to scan and index prospective business data.
Created custom recommendation engines mapping lead signals to hyper-personalized outreach strategies.
Streamlined business development workflow, reducing manual prospecting by over 80%.
Head of Technology Transformation / System Designer • Ready to use
An analytical agentic engine (also referred to as Brand Assessment) that measures a brand's market share, sentiment score, and cross-channel visibility, providing direct recommendations for optimization.
Designed NLP frameworks to analyze social, search, and marketing signals.
Developed an automated branding scorecard detailing actionable, prioritized improvements.
Provided clear visual representation of brand metrics for marketing executives.
An innovative AI marketplace allowing companies to post complex, unsolved problems that are matched with skilled workers, converting solutions into potentially investable business opportunities.
Integrated intelligent matching models to connect project requirements with expert profiles.
Architected strategic partnership frameworks enabling independent workers on the platform to obtain health insurance.
Created a framework that nurtures individual worker contributions into seed-investable ventures.
An advanced athletic-intelligence framework designed to aggregate, unify, and analyze cycling race data across the global peloton with a single source of truth.
Architected telemetry parser systems unifying disparate cycling race metrics.
Minerva Tanglao Ott (Minnie) is a Silicon Valley engineering leader, Head of Technology at Creative Blue, and Senior Technical Program Management (TPM) leader with 20+ years of executive experience across Google, Apple, Sun Microsystems, and enterprise startups.
What was Minerva Tanglao Ott's role at Google and Apple?
At Google (2011–2025), Minnie served as Senior Engineering Program Manager leading Google Maps Voice Navigation integrated with Gemini AI, Service Desk infrastructure for 150,000+ employees, and co-founding Stanford LEAD @ Google. At Apple (2009–2011), she managed global IS&T software releases including the Apple Job Search portal localized across 80+ countries.
What AI platforms has Minnie Ott developed at Creative Blue?
At Creative Blue, Minnie architected GrowthOS (an enterprise AI operations platform), Lead Generator (autonomous AI sales prospecting agent), and Brand Assessment (NLP sentiment and brand equity dashboard).
How can I schedule a consultation or meeting with Minnie Ott?
•
Generative AI • 6 min read •
By Minerva Tanglao Ott (Minnie)
GenAI Built This Website, Chatbot, & Blogger
An inside look at how AI coding tools like Lovable, Claude Desktop, and Gemini powered the end-to-end creation of this portfolio website, Mochi AI companion, and blogging engine.
Building modern software is undergoing a massive paradigm shift. As a Technology Transformation Leader and former Google Maps & GCP Engineering TPM, I wanted this portfolio website to be more than just a static resume. It needed to serve as a living, breathing demonstration of modern Agentic AI, natural language app building, and human-in-the-loop engineering.
In this article, I share how Generative AI tools—specifically Lovable, Claude Desktop, and Google Gemini—were leveraged to rapidly build, iterate, and deploy this complete full-stack web application, interactive AI companion (Mochi 🥞), and automated blogging engine.
Phase 1: Rapid Full-Stack Prototyping with Lovable
When starting the web app, speed and high-quality visual scaffolding were critical. Lovable provided the initial full-stack UI framework and component architecture using React, Vite, and Tailwind CSS.
Using natural language prompts, Lovable helped establish:
Modern Minimalist Aesthetics: Clean, high-contrast typography, generous negative space, and structured section cards for Experience, Work, and Credentials.
Component Modularity: Decoupled UI modules including the interactive header, smooth section routing, and responsive mobile layouts.
Phase 2: Refinement & Complex Logic with Claude Desktop
As the codebase evolved beyond front-end layout into deep state management, server-side express routing, and custom SEO static site generation, Claude Desktop became the primary driver for heavy technical engineering.
Claude Desktop orchestrated:
Full-Stack Node/Express API Server: Implementing backend API routes (/api/posts, /api/subscribe, /api/send-email, /api/auth/verify) that proxy third-party SDK calls securely without exposing API keys to the browser.
Dynamic SEO & Search Indexing: Generating pre-rendered index-seo.html pages and a dynamic /llms.txt endpoint so AI search engines and web crawlers can index every portfolio achievement and blog article seamlessly.
Mochi 🥞 AI Companion: Integrating Google Gemini 2.5 Flash on the backend to power Mochi, an interactive AI companion trained on my full career background, technical patents, publication history, and project milestones.
Author Studio & Blogging Engine: A secure admin blogging studio backed by Firebase Authentication and Firestore, enabling real-time article publishing, markdown rendering, and local-storage fallback sync.
Key Takeaways for Tech Leaders
Building software with GenAI isn't about replacing human engineering—it's about amplifying human intent. By combining natural language generation with rigorous architectural oversight:
Time-to-market drops drastically: What used to take weeks of boilerplate setup now takes hours.
Human-in-the-loop governance is essential: AI tools generate code fast, but human direction ensures accessibility, security, error boundary handling, and precise domain fidelity.
If you are exploring how agentic AI and LLM workflows can transform your engineering teams or digital product strategy, feel free to reach out via my Contact page or book a 1:1 advisory appointment!
•
Leadership • 5 min read •
By Minerva Tanglao Ott (Minnie)
The Art of Scaling Engineering Teams without Chaos
How to transition from a chaotic start-up style execution to a highly structured, scalable Technical Program Management (TPM) framework.
Scaling an engineering organization is one of the most delicate challenges a Head of Technology or Senior TPM will face. In the early stages, speed is everything. Communication happens organically across a single room or Slack channel.
However, once an organization grows past 50+ engineers, the organic model breaks. What worked before—handshakes, ad-hoc planning, and tribal knowledge—suddenly turns into bottlenecks and misalignment.
1. Establish Structured TPM Frameworks
Technical Program Management isn't about adding bureaucratic overhead; it's about removing friction. By introducing light, standardized agile/scrum processes, clear milestone mapping, and predictable release cadences, teams can regain focus.
RACI Matrices: Define clearly who is Responsible, Accountable, Consulted, and Informed for each deliverable.
Core Milestones: Define common entrance/exit criteria for project stages (Concept, Design, Implementation, Launch).
2. Guard the Engineering Focus
As teams grow, the volume of meetings expands exponentially. A key objective for leaders is to establish meeting-free blocks, consolidate standups, and shield engineers from unnecessary cross-departmental noise.
3. Clear Ownership and Domain Boundaries
Divide monolith teams into specialized squads with clear domain ownership. If multiple squads are constantly editing the same services, merge conflicts and deployment delays will skyrocket. Decouple your system architecture alongside your organizational structure.
•
Technology • 6 min read •
By Minerva Tanglao Ott (Minnie)
Migrating Legacy Architectures: A Head of Tech's Playbook
A tactical playbook for de-risking high-stakes database and infrastructure migrations in live production environments.
Infrastructure and architectural migrations are notoriously risky. We've all heard the horror stories: database migrations gone wrong resulting in hours of unscheduled downtime, or major refactors that introduce regression bugs across critical modules.
But as technology matures, staying on legacy systems becomes a security threat, a scalability bottleneck, and a drag on team morale. Here is my executive playbook for executing high-stakes migrations with zero user impact.
Step 1: The Strangler Fig Pattern
Never attempt a "Big Bang" migration where you flip a switch and shift 100% of traffic to a new service on a Sunday night. Instead, use the Strangler Fig pattern. Build your new microservices alongside the legacy monolith, and intercept requests, routing them incrementally to the new system.
Step 2: Establish Comprehensive Observability
Before changing a single line of backend routing, establish precise performance, error, and throughput metrics.
Shadow Deployments: Route a copy of live production traffic to the new database/service without using its responses. Compare results and performance profiles in real time.
Canary Launches: Shift 1% of live traffic, then 5%, then 25%, monitoring logs closely for anomalies.
Step 3: Align Business Stakeholders
Migrations are technical, but their success depends on organizational alignment. Communicate technical benefits in business terms: reduced hosting costs, improved uptime SLA, and faster feature delivery times.
•
Engineering • 4 min read •
By Minerva Tanglao Ott (Minnie)
Standardizing Core JMX Patterns in Enterprise Systems
Deep dive into Java Management Extensions (JMX) instrumentation, monitoring, and structural validation of core patterns.
As the Technical Editor of JMX Programming, I spent significant time analyzing how large enterprise systems instrument their services. Java Management Extensions (JMX) provide a standard, robust architecture to monitor resources, load-balance services, and manage configurations dynamically at runtime.
Why JMX Matters in Modern Enterprise
While microservice ecosystems have heavily embraced HTTP/JSON telemetry endpoints (like Prometheus metrics), JMX remains the gold standard for deep JVM monitoring and interactive runtime manipulation.
With JMX, you don't just view metrics; you can invoke operations dynamically—such as forcing garbage collection, reloading system configurations, or modifying thread pool size on the fly without restarting the application.
Designing MBeans (Managed Beans)
When designing manageable resources, there are standard patterns to follow:
Standard MBeans: Defined by writing a Java interface whose name ends in MBean and a class implementing that interface.
Dynamic MBeans: Implemented by providing a generic metadata structure. This is highly useful for dynamic scripting engines or wrapper adapters.
Security and Authentication
Never expose JMX ports without strict SSL and username/password verification. In cluster environments, wrap JMX adapters with firewalled secure channels to prevent arbitrary remote code execution.
