Milan Barot

Real-time context engine (aka Job Bandit) is a desktop productivity application designed to help engineers prepare for technical interviews through AI-assisted practice and contextual guidance. The application provides a streamlined interface for interacting with modern large language models, allowing users to explore coding problems, system design scenarios, and technical concepts while grounding responses in their personal resume and target job descriptions. Built with Electron and a local FastAPI backend, the system acts as an orchestration layer between the user and advanced LLM APIs such as OpenAI and Claude. The application synthesizes prompts dynamically by combining conversation context, uploaded professional background, and job requirements to produce more relevant responses. Streaming API integrations allow responses to be delivered in real time while tracking metrics such as time-to-first-token latency and session performance.

The platform includes structured session management, searchable conversation history with syntax-highlighted code blocks, and exportable transcripts for later review. It also supports visual queries through screenshot analysis and provides configurable interface modes that allow users to keep the assistant accessible while practicing coding problems or reviewing system design scenarios.

Additional features include secure local storage, hardware-based licensing validation, and an extensible architecture designed to support multiple AI providers and future model integrations. The goal of the project is to explore how modern AI systems can augment technical learning workflows and provide a more interactive approach to interview preparation.

tech/tools: Python, JavaScript, HTML5, CSS/SASS, Electron, glassmorphism UI, GPT-4, GPT-5 (nano, mini), OpenAI API, Hardware-locked licensing, Markdown export.

The “Monthly Music Sync” project is designed with the goal of synchronizing a user's liked songs between YouTube Music and Spotify platforms using Python and relevant APIs. The ELT pipeline is built using Python, AWS S3, YouTube Data API, Spotify API, and integrates with third-party libraries such as spotipy and youtube_dl. The project retrieves music data from YouTube Music and Spotify using their respective APIs and processes this data by extracting metadata and searching for corresponding tracks on Spotify. The processed data, specifically YouTube video IDs, are stored in an AWS S3 bucket for tracking synchronized songs.
The pipeline is automated to run monthly via Apache Airflow, ensuring periodic synchronization of liked songs.
Future enhancements include further integration with Apache Airflow for automation, development of a React App for a user-friendly interface, and enhanced error handling mechanisms.

python/libraries: spotipy, youtube_dl, Pandas, plotly.
tech/tools: Python, AWS S3, YouTube Data API, Spotify API, Apache Airflow, React (for future enhancements)

With the aim of retrieving the user's music data via Python's REST API and staging it to the Postgres server, the ELT pipeline has been designed using a combination of Python, Postgres database, Dagster for automation, docker for containerization, and Power BI for visualization. The data is transformed in a number of ways using pandas to produce useful insights that can be used to build PowerBI dashboards and visualizations. With Dagster, the pipeline is automated to run every month. The project's ultimate objective is to discover the user's monthly music preferences by analyzing particular characteristics, such as the recently released "audio-features" by Spotify.

python/libraries: Pandas, plotly, urllib, pandasql, request.
tech/tools: Dagster, Docker containers, Power BI

An interactive “University” website clone developed using using PHP, JavaScript, WordPress theming & the WordPress REST API.

Created “like” feature, “Live/on the fly” search functionality, WordPress custom field Post types using JavaScript and JQuery which was then connected to the backend using JSON and WordPress Rest APIs and created raw JSON data.
libraries: Gulp, JQuery, Bootstrap, WordPress RestAPI, Font Awesome
tech/tools: HTML, SASS, JavaScript, PHP, WordPress, Git

I am a Data Engineer with 6+ years of experience specializing in the architecture and maintenance of high-stakes data pipelines within the healthcare, real estate, and telecom sectors. My expertise lies in bridging the gap between legacy infrastructure—like the complex EDI and CMS workflows I managed at Blue Cross Blue Shield—and modern cloud-native solutions using Azure Databricks and LLM orchestration.

I am a hands-on builder who values operational stability as much as innovation. I've led offshore teams through high-pressure recovery efforts and managed over 30 production deployments in environments where data accuracy is a regulatory requirement. I approach engineering with a product-focused mindset, recently developing custom AI-driven applications that utilize low-latency streaming and RSA security to solve real-world efficiency gaps.

Here is my downloadable resume.