Offerlander.ai is a revolutionary platform designed to accelerate your job search by providing customized career preparation tools, including AI-driven chatbots for content generation and cover letter creation. Imagine having a personal assistant who understands your unique career aspirations and tailors every piece of content to highlight your strengths. With Offerlander.ai, you can effortlessly generate professional, compelling cover letters and other job application materials that stand out to potential employers. The platform’s intelligent chatbots guide you through each step, offering insights and suggestions to enhance your profile and increase your chances of landing your dream job. By leveraging advanced AI technology, Offerlander.ai not only saves you time but also ensures that you present the best version of yourself to recruiters, making your job search more efficient and effective.

SecurityMaster:- Led a system redesign that significantly improved system performance by reducing cache data staleness ratio by 86.77%, achieved through the strategic overhaul of schema design and messaging strategies.- Developed an innovative cache invalidation and printing feature in a debugger tool using messaging queues, catalyzing a productivity boost for the internal engineering team.- Established a robust service and library monitoring pipeline utilizing GUTS metrics and logs, delivering enhanced transparency in system performance.- Spearheaded a messaging strategy redesign for the system, ensuring high fanout and reliable data distribution, which ultimately bolstered system responsiveness and stability.- Maintaining database and sharing database to achieve high availability and consistency.Tech stack: C++, Python, JS, TS, Memcached, Redis, Kafka, MBUS, Splunk, Humio, SQL Server, ComDB2.

- Directed the construction of distributed data infrastructure and pipelines, simultaneously managed company-wide data quality, guaranteeing data accuracy and consistency.- Boosted the efficiency of the streaming pipeline by parsing terabytes of data, leading to the successful deployment of over five parsers into production and significantly enhancing data processing speed and reliability.- Crafted and deployed two additional REST API endpoints on MySQL and MongoDB, augmenting system functionality and offering users expedited data access.- Devised over three dashboards for meticulous data quality monitoring, established crucial metrics, and set up alert systems for immediate notifications. Additionally, introduced an automated quality check framework for ETL testing with Python and Great Expectations within Databricks.- Conducted a comprehensive investigation of data inconsistencies, pinpointing and troubleshooting over 50 pipeline and cluster issues. Provided vital technical solutions to the internal team and more than five customers by creating over 20 intermediate tables, markedly improving data reliability and customer satisfaction.- Generated a savings of over $250k and additional revenue in the last quarter by enhancing the efficiency and performance of the data pipeline.• Techniques: Python, SQL, Kubernetes, Databricks, Spark, Airflow, Looker, MongoDB, RabbitMQ, Kafka, Cassandra, S3 and Lambda.

- In charge of 3 data pipelines with SQL Server, S3 and migrated >250k data into CrimeView database.- Designed data schema, built >15 tables and performed data modeling cooperatively with developers inSisense by connecting >5 data sources to serve data analytics purpose and machine learning pipelines.- Owned quality of all data pipelines and dashboards by performing data testing and UI testing by Selenium.- Built 3 quality monitoring dashboards with alerts, pinpointed >35 issues and provided technical solutions.- Helped reduce >$100k cost by improving data quality within 3 month.

- Collected >12k fruit images and pipelined them into S3 bucket to be consumed for image classification.- Converted the images into numerical data with OpenCV in Python and saved the data into database.- Analyzed data attributes, feature relationships, utilized principal component analysis (PCA) to extractmajor features and utilized normalization, standardization to rescale data and feed data into ML models.- Developed an optimal model based on image attributes, improving classification accuracy up to 96.7%.- ML techniques used: Random Forest, Logistic Regression, K-Nearest Neighbors, Support Vector Machine.