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Schedule Interview NowMy name is Nashit M. and I have over 10 years of experience in the tech industry. I specialize in the following technologies: Python, pandas, Databricks Platform, Apache Spark, SQL, etc.. I hold a degree in Bachelor of Science (BS). Some of the notable projects I’ve worked on include: Upwork-Slackbot, INarrator, Tool for Langchain Main Repo: Google Scholar, Spark Streaming Cluster for Fintech Company, Reddit Spark Pipeline, etc.. I am based in Lahore, Pakistan. I've successfully completed 7 projects while developing at Softaims.
My passion is building solutions that are not only technically sound but also deliver an exceptional user experience (UX). I constantly advocate for user-centered design principles, ensuring that the final product is intuitive, accessible, and solves real user problems effectively. I bridge the gap between technical possibilities and the overall product vision.
Working within the Softaims team, I contribute by bringing a perspective that integrates business goals with technical constraints, resulting in solutions that are both practical and innovative. I have a strong track record of rapidly prototyping and iterating based on feedback to drive optimal solution fit.
I’m committed to contributing to a positive and collaborative team environment, sharing knowledge, and helping colleagues grow their skills, all while pushing the boundaries of what's possible in solution development.
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Techlogix
Engineered a Slackbot that, given a query, listed the latest jobs for the user in the form of a cleaned list with summarized job details. The bot was interactive within Slack, allowing users to quickly overview jobs on SoftAims relevant to their query.
INarrator is a python package developed using langchain that allows user to summarize their emails from various providers using the power of LLMs.
Engineered the following Open-Source Google Scholar AI Tool utilized by 1000+ developers.
The Spark Streaming Open Source Cluster on Kubernetes project was a success, resulting in a highly scalable, fault-tolerant, and efficient platform for processing real-time data streams The project involved creating a Spark Streaming cluster running on a Kubernetes cluster, with the goal of processing continuous streams of data in real time. The project was broken down into several key steps. The first step was to create a Kubernetes cluster and configure it to be able to run Spark Streaming applications.
Engineered a real-time data pipeline in Spark to ingest data from Reddit. Deployed the pipeline using Airflow, with data storage managed in Apache Iceberg.
Bachelor of Science (BS) in Computer science