Saleem Khan

A Chrome Extension (Manifest V3) that tracks active browsing time per site, automatically categorises every domain into one of 11 categories (Technology, Entertainment, Productivity, and more), and surfaces the data through a clean popup with daily history and search. For sites that cannot be classified by rule, it falls back to Amazon Bedrock via a serverless AWS pipeline — keeping the API key entirely server-side. Built with pure vanilla JavaScript; no build step, no dependencies.

Chrome Extension rule engine (local, no network) → cache miss triggers AI path: CloudFront → Lambda@Edge (auth) → API Gateway → Lambda → Amazon Bedrock (Claude) → category returned → cached in Chrome storage so the same domain is never sent twice.

A cross-platform iOS & Android app (React Native / Expo) that uses the device camera to photograph waste items and classifies which bin they belong to using Amazon Bedrock's vision AI. Advice is tailored to the user's Australian state and council — covering all eight kerbside bin categories (general waste, mixed recycling, organics/FOGO, glass, drop-off, reuse, and more). Scan history is stored locally on-device. No login or account required. Images are deleted from S3 immediately after processing; job records in DynamoDB expire after 24 hours.

*Disclaimer: SecureBin is an independent application and is not affiliated with any government entity.

Mobile app → CloudFront + OAC (SigV4) → Lambda presign URL → direct S3 PUT → S3 event triggers Processor Lambda → Amazon Bedrock APAC cross-region inference (Sydney + Melbourne) → DynamoDB result → app polls → displays item name + bin colour.

A serverless web application that uses Amazon Textract and Bedrock to instantly extract line items from receipts (images or PDFs) and splits costs across multiple people. Features real-time calculation, tax/discount allocation, and secure authentication via Cognito.

Static frontend (S3/CloudFront) → Cognito Auth → Lambda Function URL → Textract (Async for PDFs) → Amazon Bedrock (Claude) → DynamoDB for state persistence.

ResearchSecure is a Windows-based tracking application deployed on panelist machines to silently monitor and capture user activity for behavioural research and analytics. I was responsible for designing, implementing, maintaining, and enhancing critical application modules to enable secure, efficient, and unobtrusive data collection. My work involved close collaboration with cross-functional teams to improve the application's performance, reliability, and scalability, all while ensuring strict adherence to data privacy and security standards.

Additionally, contributed to the maintenance and support of Comscore's legacy Windows applications, ensuring stability, compatibility, and ongoing performance improvements.

• Served as Senior Engineer and responsible for the development and maintenance of major modules within the ResearchSecure application for Windows 10 and 11 supporting both x86 and x64 architectures.
• Engaged in end-to-end requirement analysis, actively collaborating with stakeholders to define application architecture and implementation strategies.
• Authored, updated, and maintained comprehensive technical documentation using iShare and Confluence.
• Identified opportunities for process improvement by utilising tools like WinDbg to analyse production crash dumps and resolve critical issues.

Netsight Chromebook Meter is a key component of Nielsen's Audience Measurement suite within the Nielsen Digital Panel portfolio. It is designed to unobtrusively capture detailed panelist activity on Chromebooks without disrupting the user experience. The collected behavioural data is securely transmitted to AWS S3 for further processing and storage.

• Served as the primary designer and developer of the Chromebook Extension Meter, leading its development from concept to production deployment.
• Worked within an Agile environment, contributing to sprint-based development cycles and continuous delivery.
• Actively participated in requirements analysis by collaborating with stakeholders, proposing technical solutions, and implementing them effectively.
• Designed and built the extension from the ground up, ensuring robust functionality, performance, and user transparency.
• Authored and maintained comprehensive technical documentation and operational procedures using Confluence.

Netsight Meter is a core component of Nielsen's Audience Measurement platform under the Nielsen Digital Panel portfolio. This Windows-based application is designed to unobtrusively log detailed panelist activity on their computers without disrupting their experience. Collected data is securely uploaded to AWS S3 for processing and storage.

• Served as Senior Engineer and Technical Lead, responsible for the development and maintenance of multiple modules within the Netsight Meter application across Windows 7, 8.1, 10, and 11, supporting both x86 and x64 architectures.
• Led Agile-based development efforts, including sprint planning, backlog grooming, sprint board management, and daily stand-up meetings.
• Engaged in end-to-end requirement analysis, actively collaborating with stakeholders — including business teams, subject matter experts, and downstream consumers — to define application architecture and implementation strategies.
• Authored, updated, and maintained comprehensive technical documentation and operational procedures using iShare and Confluence.
• Identified opportunities for process improvement through FMEA (Failure Mode and Effects Analysis), and utilised tools like WinDbg to analyse production crash dumps and resolve critical issues.
• Provided technical mentorship to team members, conducted code reviews, and supported skill development to ensure high code quality and team growth.

Netsight Meter is a core component of Nielsen's Audience Measurement platform under the Nielsen Digital Panel portfolio. This Windows-based application is designed to unobtrusively log detailed panelist activity on their computers without disrupting their experience.

• Served as Senior Engineer and responsible for the development and maintenance of multiple modules within the Netsight Meter application across Windows Vista, 7 and 8.1, supporting both x86 and x64 architectures.
• Engaged in end-to-end requirement analysis, actively collaborating with stakeholders — including business teams, subject matter experts, and downstream consumers — to define application architecture and implementation strategies.
• Authored, updated, and maintained comprehensive technical documentation and operational procedures using iShare and Confluence.
• Identified opportunities for process improvement through FMEA (Failure Mode and Effects Analysis), and utilised tools like WinDbg to analyse production crash dumps and resolve critical issues.
• Provided technical mentorship to team members, conducted code reviews, and supported skill development to ensure high code quality and team growth.

Exchange Connectivity (Order Router-OR) connects the Morgan Stanley internal systems to Trading Exchanges across Asian, European and US regions.

• Software development and maintenance of various exchange connectivity equity trading system (ETS) applications for Morgan Stanley.
• Extensively worked for the requirement analysis, interacting with exchange developers and their respective implementations specifically BSE, EUWAX, XQS, LIFFE, EUREX, XQS and BSE.
• Engaged with stakeholders such as end-users and business analysts for finalising application design, workflows and reports.
• Writing and maintaining application development documentation and operational procedures.

Vaachak is a Windows/Linux based text-to-speech (TTS) engine developed using C++. TTS is a speech conversion technology that enables automatic systems to convert text to natural-sounding speech. Prologix was first acquired by Nuance, which was later acquired by Microsoft.

• Researched, Designed and developed text-to-speech synthesis based on Harmonic Plus Noise Model (HNM) in Matlab and C++.
• Developed a framework to build HNM based parameters to be used as TTS solutions for Indian Languages which is far less in size as compared to traditional sound concatenation based on phonemes and syllables.
• Constantly demonstrated improvement in the quality and performance of speech sound by R&D.

Spoon Feeding Robot System was a speech-enabled CSIR Network project, developed to assist physically challenged persons with feeding.

• Researched, designed and implemented the hidden Markov Model based spoken word recognition.
• Developed a framework to build HMM based parameters to be used for spoken word recognition.
• Written and published multiple research papers on LPC and HMM based speech recognition.