VISHNUPRIYA PRADEEP
Seattle
Summary
Applied AI Engineer with 8+ years building production machine learning systems, reusable platforms, experimentation infrastructure, and recommender and NLP applications. Owned machine learning system architecture from problem definition through evaluation, deployment, and production operations. Led cross-functional technical and project execution and mentored engineers to reduce delivery bottlenecks.
Overview
9
9
years of professional experience
Work History
Co-Founder & Machine Learning Engineer
Veriswell
Seattle
09.2026 - Current
- Co-founded and launched a household nutrition platform serving [X households/Y users], generating personalized shared meal plans across individual nutritional, allergy and cultural constraints.
- Architected a multi-objective recommendation system ranking recipes by nutritional suitability, household preferences, diversity, preparation constraints and historical acceptance.
- Built a recipe-intelligence pipeline covering [X recipes], including ingredient normalization, semantic retrieval, dietary classification, recipe similarity and substitution recommendations.
- Developed offline evaluation across ranking quality, coverage, diversity and constraint violations, improving [metric] by X% over a popularity/rule-based baseline.
- Instrumented recommendation acceptance, meal completion, substitutions and weekly retention to support product experimentation and model improvement.
- Collaborated with dietitians and users to translate nutrition requirements and household behavior into product constraints and evaluation criteria.
Machine Learning Engineer
THE WEATHER COMPANY
01.2022 - 02.2026
- Project and technical lead: AI architecture, experimentation, platform design, and production delivery
- Led applied AI projects end to end, establishing technical direction and ensuring successful delivery from problem framing and architecture through evaluation, deployment, and production operations.
- Defined the architecture and built production systems for predictive modeling, time-series forecasting, experimentation, and customer segmentation.
- Architected and built a schema-driven ML platform that converted recurring project-specific development into reusable, configurable workflows, allowing teams to launch new use cases without rebuilding pipelines.
- Architected and built the company's A/B testing platform, standardizing experiment randomization, primary and guardrail metrics, statistical significance testing, confidence intervals, and evaluation across product initiatives.
- Conducted cohort, funnel, retention, and conversion analyses using PySpark for large datasets, identifying behavioral patterns and opportunities to improve product outcomes. Setup amplitude dashboard for the experiments.
- Engineered behavioral and contextual features for downstream ML models and predictive applications.
- Led the design and implementation of a flu-prediction system that combined CDC surveillance data with weather signals to forecast changing flu patterns.
- Designed and built an automated customer-segmentation system for audiences including people with asthma or obesity, parents, and pet parents, and making new segments easier to create and maintain.
- Selected modeling approaches based on the problem and available data, including traditional ML, deep learning, self-supervised and semi-supervised learning, fine-tuning, and rule-based methods.
- Measured the post-launch business effect of product and model changes and presented findings, tradeoffs, and recommendations to directors, executives, and cross-functional stakeholders.
- Owned releases across development, QA, and production environments, including validation, deployment coordination, troubleshooting, and resolution of operational issues.
- Optimized codebase for improved scalability and efficiency of automated processes.
- Developed APIs for integrating third-party AI services into existing applications.
- Created graphs and charts detailing data analysis results.
- Applied feature selection algorithms to predict potential outcomes.
NLP Software Engineer
IBM - THE WEATHER COMPANY
Atlanta
07.2017 - 01.2022
- Led Watson Ads NLP and ML projects for mood analysis, customer mood prediction, audience segmentation, and contextual targeting.
- Defined system architecture for scalable model training, evaluation, and deployment supporting Watson Advertising experiences.
- Developed supervised and unsupervised ML solutions, leading projects from requirements through modeling and deployment.
- Collaborated with stakeholders to translate ambiguous requirements into ML problems and present recommendations.
- Mentored engineers in NLP modeling, system design, testing, and production readiness practices.
- Created IBM Watson-powered conversational advertising experiences and product recommender systems for clients.
- Designed NLP algorithms for contextual targeting, keyword generation, and topic modeling.
- Initiated workflow automation increasing delivery of conversational advertisements from three to 12 within three months.
- Technically led an advertising-fairness initiative evaluating ad-delivery outcomes across Nielsen, Comscore, Lotame and internally developed audience segments; guided disparity analysis across race, age and geography, identification of contributing features, application of bias-mitigation approaches, and evaluation of their effect on campaign delivery.
- Work developed under this initiative contributed to IBM Advertising’s AI fairness technology and was showcased at the 2022 Cannes Lions International Festival of Creativity.
Education
M.S. - Computational Science and Engineering
Georgia Institute of Technology
12-2015
M.S. - Environmental Engineering
Georgia Institute of Technology
05-2014
B.Tech. - Bioinformatics
Vellore Institute of Technology
07-2011
Skills
- Machine Learning Time series, recommender systems, customer segmentation, NLP, deep learning, self-supervised learning, semi-supervised learning, fine-tuning, knowledge graphs, rule-based models, model evaluation
- Experimentation and Analytics A/B testing, experiment randomization, guardrail metrics, statistical significance testing, confidence intervals, cohort, funnel, retention, and conversion analysis
- Programming and Data Python, Java, SQL, PySpark, pandas, NumPy, scikit-learn, TensorFlow, OpenCV, NLTK, MongoDB
- Production ML Model deployment, dev/QA/production promotion, release validation, API integration, reusable schema-driven workflows, production support
- Systems architecture and software engineering
Accomplishments
- Outstanding Technical Achievement Award for Watson Ads Optimization, November 2018, Watson.
- Advertising Augmented Excellence Award, February 2021.
Timeline
Co-Founder & Machine Learning Engineer
Veriswell
09.2026 - Current
Machine Learning Engineer
THE WEATHER COMPANY
01.2022 - 02.2026
NLP Software Engineer
IBM - THE WEATHER COMPANY
07.2017 - 01.2022
M.S. - Computational Science and Engineering
Georgia Institute of Technology
M.S. - Environmental Engineering
Georgia Institute of Technology
B.Tech. - Bioinformatics
Vellore Institute of Technology