Muhammad Talha Zubair

• Contributed as a back-end engineer to a client project focused on voice cloning.
• Collected and refined reference voice samples using Amazon Transcribe from a former team member’s YouTube content.
• Utilized curated samples for voice cloning with industry-leading services, including ElevenLabs, Voice AI, and Speechify.
• Explored open-source models such as F5-TTS and OpenVoice, though their performance did not meet the required standards.

• Worked with Libertify, a pioneer in transforming documents into AI-powered, interactive experiences, focusing on enhancing the retrieval-augmented generation (RAG) application.
• Integrated PGVector as the vector database, optimizing similarity search and embedding management for improved scalability and efficiency.
• Designed and implemented an LLM-agnostic service, enabling seamless integration with various large language models, including OpenAI's GPT, ensuring flexibility for diverse client needs.
• Enhanced the PDF parsing mechanism, enabling accurate extraction of both structured and unstructured data for better AI-driven insights.
• Integrated Amazon Transcribe and Google Text to Speech to convert speech to text, allowing the system to process voice-based inputs and improve accessibility for document analysis.
• Gained expertise in back-end development, database optimization, and AI-based SaaS solutions, particularly in regulated domains, delivering impactful results in document-based knowledge transformation.

Developed a real-time insights platform for financial advisors to analyze corporate earnings calls. Responsibilities included:

• Designed and developed back-end services using Django with WebSocket support to enable real-time interaction between front-end applications and the FinnHub API.
• Implemented live audio transcription feature using OpenAI Whisper models to transcribe earnings calls in real time.
• Developed sentiment analysis pipelines focused on dynamically detecting sentiment based on specific financial keywords mentioned during earnings calls.
• Built investor reaction modules to capture and display market sentiment indicators during live and recorded calls.
• Integrated a Retrieval-Augmented Generation (RAG) pipeline to enable intelligent querying and interaction over financial data, enhancing decision-making insights for advisors.
• Created a robust, scalable architecture simulating real-time data streams using cached datasets, ensuring demo flexibility and realism.
• Ensured seamless multimodal data processing by combining live voice transcriptions, textual financial reports, and real-time stock market data into a unified, interactive user experience.

A GenAI-powered solution that can automate SOP update, reducing manual efforts and improving compliance adherence for diagnostics companies. Automatic updates to the existing SOPs using their video calls and audio transcripts

• Implemented retrieval-augmented generation (RAG) to enhance the question-answering system's performance.
• Utilized GPT-3.5 and FAISS for efficient document retrieval from a diverse corpus, including technical manuals, academic papers, business reports, and historical documents.
• Stored extracted information in FAISS to ensure quick access to the most relevant documents.
• Addressed challenges related to handling large data volumes and ensuring high-quality, contextually relevant retrieval.
• Optimized the pipeline to reduce latency by refining the integration of retrieval and generation steps.
• Integrated ChromaDB for persistent storage, acting as a cache to complement FAISS and further reduce response times.
• Achieved improved accuracy and contextual relevance in answers, increasing user satisfaction with the system.

• Developed algorithms for detecting harmful cells in microscopic chicken imagery, retraining the StarDist segmentation model with Python and TensorFlow for multispecies detection.
• Built a custom Keras data generator for efficient dataset loading and preprocessing.
• Utilized OpenCV, k-means, PCA, and ResNet-50 for noise removal and image enhancement.
• Conducted data analysis using PostgreSQL and Pandas to extract meaningful insights from microscopy images.
• Implemented an MLOps pipeline with wandB for seamless dataset and model tracking.
• Integrated GitHub pre-commit hooks to enforce code formatting and maintain code quality.
• Deployed solutions using Docker and optimized version control and management with Azure DevOps.
• Enhanced Linux-based workflows by implementing bash scripts for automation.
• Established CI/CD pipelines and automated scheduling using cron jobs.
• Managed Docker containers with Kubernetes and tested deployments via Flask APIs.

• Trained the YOLOv5 computer vision algorithm using PyTorch with custom datasets across various GPU environments, including Amazon EC2 instances.
• Converted the Torch YOLOv5 model to the Core ML format for efficient inference on iOS platforms.
• Used DeepLab for segmenting different road parts and applied image up-sampling techniques via cv2 superRes.
• Developed Bash scripts and Docker pipelines to automate training and testing processes.
• Collected and managed data using PostgreSQL, with comprehensive data analysis conducted through Pandas.
• Worked extensively with Amazon EC2 and S3 to ensure efficient deployment and storage.
• Implemented unit tests and integrated them with GitHub Actions for continuous testing and deployment.
• Prioritized code optimization and maintained version control using Git for high-quality development and collaboration.
• Tested Flask API implementations rigorously to ensure reliable back-end performance and functionality.

• Designed and developed an object detection and tracking solution for thermal imagery, focusing on occlusion handling and deployment on the NVIDIA Jetson TK1.
• Trained PyTorch's YOLOv3-tiny model on a custom dataset to enhance detection accuracy.
• Created a Python-based feature-tracking pipeline using normalized cross-correlation (NCC), SIFT feature selection, and the Kalman Filter for optimal estimation.
• Analyzed detector and tracker performance, extracting valuable insights for system optimization.
• Led a four-person team, fostering collaboration between hardware and software teams and ensuring accurate object annotation for various videos.
• Deployed the Python solution on NVIDIA Jetson TK1, utilizing OpenCV for pipeline development and Numpy for efficient array operations.
• Improved performance with Cython and Numba, enabling real-time capabilities.
• Managed code versioning, reporting, and requirement tracking using Azure DevOps.
• Developed Linux-based frameworks and automated workflows using bash scripts for efficient pipeline execution.

I developed a deep reinforcement learning-based recommendation algorithm for custom users on the augmented reality game Mad Hunt using Python. I designed and deployed reward functions in reinforcement learning using TensorFlow within the existing game structure, and handled queries from Firebase and AWS platforms in Python using Firebase SDK and the Boto library.

I implemented the YOLO image recognition algorithm to facilitate real-time object detection during gameplay while researching and developing cutting-edge recommendation systems based on reinforcement learning. Exploring existing recommendation systems led to a focus on machine learning algorithms, such as collaborative filtering systems.

I also designed and implemented facial recognition solutions using algorithms like Haar cascades, MTCNN, and FaceNet in TensorFlow, deploying these on web platforms. I used Flask and NGINX for efficient web hosting and server-side functionality and Azure DevOps for code versioning, reporting, and requirements management.

To ensure the highest quality standards, I prioritized code optimization and version control using Git. Finally, I handled CRUD operations on a MySQL database to manage daily user interactions with the application.

• Researched and developed deep-learning solutions for gaze estimation, focusing on training a computer vision-based gaze detection model.
• Trained the gaze estimation model using a custom dataset developed in PyTorch.
• Utilized WandB as an MLOps tool to manage and monitor machine learning experiments effectively.
• Conducted model training and testing on Google Cloud Platform (GCP) instances, ensuring scalable compute resources for efficient performance.
• Optimized the training pipeline to enhance accuracy and reliability in gaze estimation tasks.

• Developed and implemented natural language processing (NLP) algorithms to enhance optical character recognition (OCR) systems, improving text extraction accuracy by 20%.
• Collaborated with a cross-functional team of software engineers and data scientists to optimize OCR workflows, reducing processing time by 30%.
• Conducted a comprehensive analysis of text data and improved OCR accuracy using preprocessing techniques such as tokenization, stemming, and lemmatization.
• Integrated advanced NLP techniques, including named entity recognition (NER) and sentiment analysis, to extract valuable insights from documents.
• Trained and evaluated deep learning models, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to enhance OCR performance on complex document types.

• Developed an advanced evaluation framework to assess the quality of stable diffusion-generated images.
• Leveraged Detectron2 for object detection and analysis within the generated images.
• Implemented a supervised graph approach, incorporating a relative size graph to evaluate object realism.
• Enhanced the assessment process by ensuring accurate determination of object proportions and visual fidelity.

• Researched and implemented an LSTM-based speaker diarization algorithm for analyzing audio recordings in conversational AI applications.
• Preprocessed audio data by extracting key acoustic features, normalizing speech signals, and reducing background noise for improved diarization accuracy.
• Fine-tuned diarization models to segment and label multiple speakers in parliamentary debates and call center conversations.
• Optimized speaker clustering methods by handling overlapping speech, adjusting model parameters, and refining segmentation accuracy.
• Developed a user-friendly interface to display speaker-segmented transcriptions, facilitating easier analysis of structured discussions.
• Contributed to government debate analysis by implementing diarization models that classified speech based on ministries, improving the categorization of legislative discussions.