Md Sajid Khan

Design, train, and deploy production-grade machine learning models using AWS Own the full ML lifecycle, from development to secure cloud deployment Build solutions across computer vision, predictive analytics, and generative AI Implement MLOps pipelines using AWS SageMaker, Terraform, CloudFormation, and Jenkins Integrate models with core AWS services such as EC2, Lambda, IAM, S3, and RDS Ensure reproducibility, governance, and auditability in a regulated healthcare environment Collaborate in Agile teams using JIRA, Git, and peer review processes

Engaging in the verification and validation of coding outputs from LLMs to ensure accuracy and reliability in model performance Assisting in the development of automated validation processes to enhance efficiency in quality assurance workflows Collaborating with cross-functional teams to gain insights into best practices for code validation and quality improvement Technology: Python, NumPy, Pandas, Scikit-learn, various programming languages, and tools

As a founding member and technical lead, I wear multiple hats - shaping product vision, leading end-to-end software development, and driving innovation to solve real-world problems. 🔹 Spearheaded the design and architecture of our core platform, ensuring scalability, reliability, and performance. 🔹 Built and managed a cross-functional development team, establishing coding standards, agile workflows, and CI/CD practices 🔹 Oversaw full-stack development using [insert your tech stack: e.g., Python, React, Node.js, MongoDB, AWS, etc.], delivering high-quality features on time 🔹 Collaborated closely with product, design, and business teams to align development with market needs and user feedback 🔹 Handled technical decision-making, mentorship, code reviews, and production deployments 🔹 Drove MVP launch and ongoing iterations

Utilized Generative AI and LLM to enhance collaboration in Prompt Engineering Fine-tuned and implemented Large Language Models (LLMs), and transformer-based architectures to enhance AI-powered applications, improving natural language processing and automation capabilities Enhanced the capabilities of LLMs for prompt engineering by integrating and optimizing for Machine Learning, resulting in a 10% improvement in model accuracy and a 15% reduction in response time Developed sentiment analysis model using Azure ML, Amazon Sage-Maker, and Hugging Face transformers for advanced NLP, achieved 85% accuracy Technology: Python, Django, FastAPI, WhyLabs, CI/CD pipelines by utilizing Github Actions, Docker, Kubernetes, NumPy,Pandas, and Scikit-learn

- Significantly contributed to extensive research on 5G network slicing using free5gc and ueransim simulators in a Linux environment, resulting in multiple published papers. - Facilitated fellow lab members in publishing conference papers, showcasing collaborative and leadership skills within the research team. - Demonstrated expertise in detecting and mitigating DoS and DDoS attacks within a 5G network slice environment, employing a proactive approach using machine learning and Python. - Applied hands-on experience in developing and deploying advanced machine learning models, integrating Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory networks (BLSTM) using TensorFlow, PyTorch, and Keras. - Specialized in utilizing CNN architectures for the detection of various network attacks, tailoring models to address specific cybersecurity challenges, and optimizing for both accuracy and computational efficiency. - Demonstrated adaptability by developing models for pattern recognition from large .csv files, showcasing a holistic understanding of diverse data formats within the cybersecurity domain. - Leveraged transfer learning techniques to adapt pre-trained CNN models to specific domains, reducing dependency on extensive labeled data and enhancing the models' ability to generalize effectively. - Proficiently implemented BLSTM networks for sequential data analysis, particularly in scenarios crucial for understanding temporal dependencies, such as attack detection based on time series data. - Applied Principal Component Analysis (PCA) for feature optimization, demonstrating commitment to enhancing the efficiency and effectiveness of machine learning models. - Utilized a range of network tools, including Wireshark and hping3, to analyze, monitor, and enhance the performance and security of the 5G network slice. - Used Python libraries such as matplotlib for effective data visualization

- Conducted extensive research on WebRTC and PION libraries, especially within the GoLang environment - Explored and investigated scalable video conferencing solutions - Researched and evaluated live video broadcasting strategies to accommodate millions of users - Investigated techniques to optimize real-time performance for seamless user experiences - Conducted research on EC2 within AWS for WebRTC video conferencing - Predicted network future conditions with 80% accuracy using BSLTM, Hadoop, Kafka

Developed a VoIP Mobile Dialer application that utilizes VoIP technology, integrates with device contacts, and supports various features such as call recording, messaging capabilities, presence status, call history/logs, customization options, and encryption protocols for secure communication. Engineered a computer vision-based barcode scanning application with 95% accuracy using ML, Python, and OpenCV Utilized Machine Learning Kit for National ID verification and facial image capture, which makes the process 90% faster than the manual one [Link: https://play.google.com/store/apps/details?id=com.alaap.app&hl=en_CA&gl=US] Led team of 4 Android developers, and 2 web developers, and collaborated with DevOps teams to successfully deliver multiple products ahead of schedule, resulting in annual cost savings of $20K. Conducted a codebase refactor, introducing runtime permissions and migrating from Java to Kotlin. Implemented modern Android development tools and components including Room, Jetpack Compose, Material Design, MVVM architecture, FCM (Firebase Cloud Messaging), 9-Patch images, and more. Customized chat features such as burn messages and schedule messages were successfully implemented. Applied various design patterns such as Factory, Abstract Factory, Singleton, Adapter, and Observer in development process.

Led Android and QA teams, while also providing assistance to Web team in creating a RESTful API, utilizing Trello for Agile project management. Engineered a computer vision-based barcode scanning application using OpenCV and Pyzbar library, capable of 95% accurately decoding barcodes Developed signup module and coupon module for a bus ticket booking system, serving a user base of 1 million. [Link: https://play.google.com/store/apps/details?id=com.shohoz.launch.consumer] Designed and implemented a complex system for booking tickets on double-decker launches/steamers. Introduced and utilized various technologies including ConstraintLayout, RecyclerView, Custom View, Volley, Gson, and other external libraries. Successfully integrated a Bluetooth printer with mobile devices to enable printing of launch deck tickets, serving ticket masters and approximately 100,000 daily users. Developed an app that seamlessly worked both offline and online, catering to Ticketmaster platform, particularly in areas with inadequate internet connectivity.

Worked on ride-sharing customer and driver app Developed credit card validation, and registration systems

Collaborated on implementation of customer-specific customization features within core system of Android. Implemented client-server functionality for Wi-Fi Direct, utilizing NFC technology to establish connections specifically on Samsung phones. Developed sample game using Chord SDK, aimed at assisting developers in understanding its capabilities. Interacted with teams from Google, Dropbox, My Magazine, and other relevant platforms to identify and resolve issues related to Samsung mobile devices. SFloater: Created application with a distinctive floating round design element positioned at top of screen, facilitating easy navigation across various settings and options. App was successfully developed and published in the Samsung App Store. Used Microsoft Excel for project report and status tracking Employed Bash scripting for Android build system