• Enhanced developer experience for AI professionals deploying cutting-edge inference solutions• Analyzed AI/ML developer trends and challenges to inform strategic product opportunities• Defined and tracked key performance metrics, leveraging customer feedback for continuous product improvement• Fostered customer relationships through leadership of AI courses, workshops, and conference presentations• Integrated emerging LLM technologies and market trends to drive product innovation• Created technical content including architecture diagrams and concept visualizations to strengthen documentation and educational materials• Developed impactful GenAI demos and technical solutions addressing real customer problems, utilizing prompt engineering, RAGs, pre-training, and fine-tuning

- Incorporated machine learning methods to improve Splunks product portfolio across the Core Platform, Applications and Solutions. - Played a key role in the evolution of Splunk’s platform and brought domain expertise in applying data-driven innovations to transform verticals such as Enterprise Security, AI Ops, and Observability.- Developed the Market & Product Requirements on how Machine Learning can be applied across the platform and our solutions to delight our customers- Collected customer, sales, partner, and market input for the development of product requirements, then develop and validate associated use cases- Refined engineering plans and roadmaps by working with R&D on an ongoing basis- Supported product marketing to develop product positioning, pricing and collateral- Served as the subject matter authority to Splunk’s field operations through product training and sales support activities

- Successfully transitioned >120 Artificial Intelligence Insight Fellows from academia and myriad industries to roles as Machine Learning Engineers and AI practitioners in the Bay Area.- Consulted with dozens of AI B2B SaaS companies in the Bay Area to define their customer needs and scope out their AI product architecture requirements, to set their customers up for success with the latest, cutting-edge advancements in AI. This was achieved while working cross-functionally with business and marketing teams, as well as Agile engineering teams.- Developed the core AI curriculum with educational resources in software engineering, ML system designs, ML platform engineering, as well as practical coding challenges.

- Supervised >100 portfolio projects in machine learning/ deep learning, computer vision projects, natural language processing, and reinforcement learning.- Onboarded dozens of Insight Fellows to building, deploying, and scaling production-level ML models with Cloud Infrastructure via Google Cloud Platform and Kubernetes.

- Managed 75+ Artificial Intelligence Insight Fellows, who are currently working as AI practitioners in the Bay Area - Supervised and contributed to 50+ deep learning projects, 30+ computer vision projects, 20+ natural language processing projects

Project Orient: An explainable and tunable movie recommender system (RS).• Developed and engineered a movie RS using the MovieLens Dataset which gives users the ability to tune the attributes that determine their movie recommendations.• Implemented user/attribute vectorization using word embeddings via Google’s Word2Vec model.• Built a matrix factorization model in Keras to predict movie ratings from user/movie embeddings.• Served via an interactive command line interface created with the Python package Click.

MS Thesis: THE CIRCUMGALACTIC MEDIUM OF HIGH-z GALAXIES: C IV AND Si IV ABSORPTION LINE STUDIES AROUND z ∼ 2−3 GALAXIESAdvisor: DR. GUILLERMO BLANC • Automated classification for the largest all-sky survey (the Sloan Digital Sky Survey, SDSS).• Trained various supervised and unsupervised ML algorithms to classify SDSS photometric data as either a Star, Galaxy or Quasar with 99% accuracy.ML/DL ALGORITHMS & STATISTICAL METHODS• Classification and regression algorithms: Linear Regression, Logistic Regression, Naive Bayes,KNeighborsClassifier, Support Vector Machines (with linear and RBF kernals), K-Means Clustering,Decision Trees, Random Forest, Dimensionality Reduction Algorithms• MCMC, Maximum Likelihood (MLE), Least-Squares linear regression, PCA, Error Propagation

• Probed the baryonic interplay between star-forming galaxies and their surrounding gaseous halos to investigate galaxy formation and evolution.• Used data extraction, reduction, and analysis methods to quantify various attributes of ionized gas.• Evaluated results against current cosmological simulations to show a discrepancy between assumed and observed properties of gas kinematics in young galaxies.