dheeraj krovvidi

AI Engineer

AspireNxt•  May 2022 - Present

- Building Pytorch models for Image Classification tasks & working on deployments, end-points using AWS. - Worked on building one-click deployment frameworks in programmatical way for: 1. Recommender systems using Python, Terraform on AWS Personalize. 2. Time Series forecasting for sales using AWS forecast 3. Anomaly Detection using Amazon L4M. * Tools / Tech Stack * : Python, Terraform, AWS services- Sage Maker, Amazon Lex, Personalize, Lambda Functions, Amazon Forecast & Anomaly Detection

Machine Learning Engineer

Applicate•  April 2020 - April 2022

- Driving projects from an architecture & framework point of view in the Machine Learning Team. - ML Framework : Worked on building the Generic machine learning frameworks for highly scalable and distribution ETL jobs with automatic re-processing and 100% automations. Used the end to end pipelines, jobs & automated systems in AI for deploying simplified solutions at scale. Serving them as a single platform in Python 3. - Sales Forecast : Designed a customized Statistical Quantile approach to forecast sales/quantity which increased model performance by 20% along with time optimization. Improved overall performances with better design choices & asynchronous behaviour enforcing state less machines and idempotent algorithms. - Recommendation Systems : Building Personalized Recommendation systems for suggestions of sales products to end-users. Used Hierarchical clustering & Ranking methods for Similarity & Relevance score generation. Used advanced parallelization method Dask to process, handle large data sets & to reduce data retrieval time by 75%. - Explainable AI (EDA) : Developed EDA Dashboards on Kibana to monitor and manage Insights of data accuracy metrics for compliance reports on regular scale. Built a customized error-metric to categorize Valid & Inconsistent data samples based on purchase pattern. For Valid cases we've achieved accuracy > 85 % and more precise for most of the clients. - Tools : Python, CI/CD pipelines (ETL jobs), Kibana, MySQL, Jenkins, AWS-S3, Machine Learning Algorithms - XGBoost , Random Forest, Other Classification & Clustering Approaches.

ML Intern

Inversion consultancy LLP•  May 2019 - August 2020

I was exposed to the various activities in the Machine Learning and Deep Learning domains using Python and TensorFlow, implementing Neural Networks, Predictive models, Regression, Recommendation Engines, and many more skills.