MLOps • Model inferencing pipeline , Model Serving and Model Training using MLFlow.• Set up the model inferencing pipeline on RTX machine using MLFlow for differenttrained / Pre-trained models for specs generation.• Worked on different models such as Dolly, FlanT5 etc.Chat on your Database• Developed a system that generates SQL queries based on user questions,executes those queries, and returns data in a tabular format.• Achieved an 80% reduction in the time developers spend creating SQL queries,significantly boosting productivity.• Implemented a feedback mechanism allowing users to dislike and correct SQLqueries they find unsatisfactory, ensuring continuous improvement and accuracy.Generative Search (RAG) • Developed a search which detects intent of user query and based on that tune theWeaviate query parameters.• Developed the indexing pipeline which fetch data from the unstructured documentssuch as word, ppt, pdfs etc.• Created a data pre-processing pipeline which cleans and create chunks for fetcheddocuments and indexed it into vector DB.• Created RESTful APIs using Django Rest Framework to facilitate seamlesscommunication for both indexing and searching processes, enhancing theaccessibility and integration of the developed functionalities. Conversational Chatbot• Utilized OpenAI to generate matrices from chat conversations, enabling seamlessintegration of conversational data into analyses.• Developed purposeful prompts in Langchain for efficient matrix construction,optimizing the process.• Leveraged conversation memory in Langchain to maintain contextual awareness,enhancing the quality and relevance of results.

1)OpenAI prompts (GPT3, GPT3.5) -> Worked on a Natural Language Text Generation project utilizing OpenAI's GPT-3 and GPT-3.5 models.-> Utilized GPT-3 text-davinci-003 and GPT-3.5's turbo model to generate effective prompts for product page content, leveraging its advanced language capabilities to produce high-quality text.2)Elasticsearch - Logstash - Kibana (ELK stack) -> Set up the entire ELK environment from scratch for production use, including Elasticsearch, Logstash, and Kibana.-> Created filter/faceted search using Elasticsearch DSL queries and aggregation for dynamic filter options.-> Implemented msearch features to increase search speed by 4x on [bestviewsreviews.com]. Developed a custom scoring function to improve search result ranking and increase search accuracy from 40% to 80%.-> Established a system to evaluate search precision and recall.-> Improved autosuggestion search quality by implementing a custom ranking function and search_as_you_type feature.-> Utilized painless scripting for multiple use cases.-> Wrote multiple logstash scripts for data indexing and created a logstash data pipeline for Elasticsearch index updates.-> Contributed to open source community in logstash-filter-aggregate plugin (https://github.com/logstash-plugins/logstash-filter-aggregate).-> Worked on Kibana dashboards and created a server monitoring pipeline using Elasticsearch beats, specifically filebeats.

Power Automate (RPA) & Microsoft Administrator • Designed and deployed a web scraping solution via Power Automate (cloud and desktop flows) to automatically extract data from specified websites, enhancing operational efficiency. • Increased team productivity by 70% by utilizing virtual machines to host the automation bot, reducing the need for individual system execution. • Implemented a mail-triggered automation system, allowing the bot to run on a virtual machine, retrieve data, and email results, with automated deallocation of resources to minimize costs.Microsoft Administrator • Administered and optimized SharePoint sites, ensuring seamless information management and collaboration. • Developed custom business solutions using Power Apps to enhance process automation and drive productivity improvements across teams.