Here i had a minor role to tranfer the data from cli instance to snowflake. The challenge over here the team was facing was to uploading the data with more than 6 million records to snowflake using kafka . Here i wrote a script in python to upload the data directly to snowflake in the cli instance

Here the role needed to fetch the data from outlook emails in pdf format and preprocess it and upload the processed data to snowflake .

Delivered an efficient,smooth pipeline to migrate data from sql server to snowflake using data lake as azure blob storage with the help of azure data factory and developed a alert system to state if the pipeline fails over platforms like email and Microsoft teams using logic apps.

Developed a Generative AI (Gen AI) solution to evaluate marketing content for auditing personnel in Asset Management Companies (AMCs). Led a team of four to create a product that validates content, ensuring desired rules and disclaimers are incorporated in images, PDFs, and videos. Integrated both generic and custom rules to perform content validation through a user-friendly interface. Ensured cloud flexibility by utilizing AWS Bedrock, Azure Open AI, and Google Gemini services. Deployed the solution using Docker, enabling efficient and scalable hosting.

Led a five-member team to develop an automated pipeline for validating email attachments in insurance policy requests. Spearheaded the integration of innovative technologies, focusing on AWS Lambda for automating email processing and reducing the need for dedicated servers. Ensured seamless client communication to align the project with expectations and address challenges. Integrated Generative AI (Gen AI) models to effectively parse diverse data from email attachments, enhancing accuracy and efficiency. Continuously refined testing methodologies to improve robustness and reliability. Significantly streamlined the policy request workflow, reducing manual intervention for insurance brokers.

Led a team to develop an AI-driven tool using Large Language Models (LLMs) to automate and enhance hiring workflows. Key Achievements: AI-Powered Screening: Introduced LLMs for autonomous candidate screening, reducing manual effort and improving matching accuracy. Candidate Ranking Algorithm: Developed an algorithm to efficiently rank candidates, streamlining the shortlisting process. Interactive Dashboard: Designed a real-time dashboard for hiring managers, offering an intuitive user experience. Feedback Loop: Implemented a mechanism to continuously improve AI predictive capabilities based on user insights. Customization and Scalability: Ensured tool adaptability for various departmental needs, enhancing versatility. Data Security: Maintained strict data privacy and legal compliance. Leadership: Fostered a creative and innovative team environment, delivering a product that elevates the hiring process.

Developed a DataOps platform to version ASR and NLP data and manage datasets for an MLOps platform. Key Achievements: Platform Development: Pioneered a DataOps platform integrating both CLI and GUI interfaces for diverse user preferences. Cross-Functional Collaboration: Identified and addressed pain points in existing MLOps workflows. CLI and GUI Design: Designed a user-friendly CLI for technical users and an intuitive GUI for non-technical users, enhancing model tracking, labeling, and monitoring. Version Control: Implemented version control and model tracking features, ensuring traceability and reproducibility of models. Training and Documentation: Authored detailed documentation and conducted training sessions for smooth team adoption. Technologies Leveraged: Programming: Python CLI Framework: Typer GUI Framework: Django, React.js Data Processing: Pandas, NumPy DevOps Tools: Git, GitHub

For a Transit Agency data platform, it is required to build ADF pipelines, To move data from mail as source to Snow ake. To upload static data les to snow ake

A micro nance corporation where their backend is implemented with SQLServer, Salesforce and Hubspot.

Implement a POC on transformations of SQLServer Tables and store them