Muhammad Umair Qadir

• Led the effort for novel research in nocturnal scratch prediction for atopic dermatitis (AD) patients, utilizing wearable actigraphy devices to capture and analyze scratch patterns during sleep.
• Developed a model using advanced machine learning techniques, including topological data analysis (TDA) and deep learning, to accurately measure scratch duration and intensity, validated against video annotations and patient-reported outcomes.
• Enhanced model generalizability by incorporating both accelerometer and gyroscope data, improving the detection of low-intensity finger scratches, and refining movement detection algorithms to reduce false negatives.
• Leveraged advanced deep learning architectures and GPT-like transformer models to process and
analyze complex biomedical sensory data, enhancing the accuracy of scratch prediction by capturing intricate temporal patterns and correlations within the data.
• Played a key role in the design and implementation of Cronus, a comprehensive predictive modeling framework built from the ground up to support the Digital Health and Biomarker Analytics Teams efforts in predictive analysis and data visualization.
• Developed and integrated core components of Cronus, including modules for feature selection, feature scaling, and data preprocessing, enhancing the efficiency and accuracy of model training and evaluation processes.
• Ensured the frameworks scalability and extensibility, allowing for seamless integration of new
algorithms and tools, including GPT-like transformer models, which empowered the team to handle
increasingly complex and large datasets with ease.

• Working as the Senior Engineer at a conversational artificial intelligence (AI) startup, have the responsibilities of product development, strategy, and deployment. The role also includes team management and creating efficient work processes.
• Successfully creating, articulating, and delivering conversational artificial intelligence product roadmaps and releases that achieve business objectives and help the engineering team in development.
• Facilitated the recruiting and hiring of five high-quality candidates along with supervising a streamlined onboarding process that achieved outstanding results with efficiency.
• Created and launched a highly scalable collaboration platform with combination of open-ai (GPT-3.5/GPT-4) & open source models to identify the intent of users text and voice, extracting insights from user long queries, summaries and metadata, which made the knowledge management process independent of human resources and shifted the learning curve from steep to gentle for new employees.
• Developed architecture for knowledge base management using off-the-shelf services like Kendra to boost the legacy knowledge transfer up to 200%.
• Created and implemented Quality of Service Agreements between System Test and internal customers, resulting in an increase of customer satisfaction from 40% to 95%.
• Implemented the processes to make the client knowledge base ready within less than 24 hours, supporting millions of documents in legacy knowledge.

• Worked as Senior Software Engineer to develop architectures and data pipelines for new services, revamping the legacy codebase to microservices, mentoring junior developers and doing peer code reviews. The role also includes project management and creating efficient work processes.
• Designed, developed and deployed the Python/ Django based WhatsApp lead tracking microservice using async message queue Kafka and inhouse data processing tools.
• Automated & digitalized the dealers (b2b customers) onboarding pipeline, signing the legal agreements and processing payments through Checkout payment gateway. This feature has increased the profits by many folds and reduced the new business onboarding time by 90%.

• Providing solutions to organizational and independent clients with annual revenue between $5 million to more than $200 million, which conserved tons of resources, time, and increased overall earnings.
• A key member of the Fin-Tech startup team that helped grow revenue by millions which was achieved by leading and delivering machine learning (ML), Deep Learning (DL) and natural language processing projects (NLP).
• Built and developed multiple teams to support business and service goals while architecting and implementing the application transformation for scalability and better client experiences.
• Designed and implemented the automation pipeline for three statement financial models that resulted in 90% less time required for processing historical data.
• Created and executed the prediction pipeline for cash flow to generate revenue projections for the next five years that reinforced the process to minimize investment risks.
• Implemented an input data pipeline between our product and different client platforms, including but not limited to QuickBooks to reduce customer onboarding time by 80% and to make the product decision-making process much more data-driven and accurate.

Tools & Technologies: Python, Machine Learning (ML), Natural Language Processing (NLP), GCP(AutoML), Flask, REST, DevOps, MySQL

● Completed the initial proof-of-concept (POC) for the projects that were tested for 100, 000+ requests in peak time. The system was able to handle 20 to 30 thousand requests per second.
● Built multiple conversational AI (chatbot) solutions from scratch to a fully functional plug-in that helped cover 70%-85% of customer care queries, which resulted in saving more than $50, 000 annually spent on customer care.
● Completed the initial POC for the more than eight projects that were tested for 100, 000+ requests during peak times. The system was able to handle 20, 000-30, 000 requests per second. Designed REST API for each to ensure seamless integration with client systems.
● Designed and developed Natural Language Processing solutions in conversational AI, text analytics, and document parsing for multiple clients saving millions in annual costs by automating the whole pipeline.
● Built an end-end architecture and pipeline for artificial intelligence (AI) enabled microservices and plugins for large enterprise products in Telecom industry
● Build and deploy Natural Language Understanding (NLU), Data Parsing, Data Warehousing and Document-AI based automated system for delivery challans and invoice parsing & mapping solution. Implemented specialized versions of these solutions for clients in the Oil & Gas industry and Retail market.

Tools & Technologies: Python, Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), GCP (functions, automl, storage, ml), AWS (lambda, s3, compute engine, api-gateway), Rasa Conversational AI, Dialogflow (Google AI), Flask, Django, MongoDB, MySQL, Git, Docker

• Completed the initial proof-of-concept (POC) for the projects that were tested for 100, 000+ requests in peak time. The system was able to handle 20 to 30 thousand requests per second.
• Built multiple conversational AI (chatbot) solutions from scratch to a fully functional plug-in that helped cover 70%-85% of customer care queries, which resulted in saving more than $50, 000 annually spent on customer care.
• Completed the initial POC for the more than eight projects that were tested for 100, 000+ requests during peak times. The system was able to handle 20, 000-30, 000 requests per second. Designed REST API for each to ensure seamless integration with client systems.
• Designed and developed Natural Language Processing solutions in conversational AI, text analytics, and document parsing for multiple clients saving millions in annual costs by automating the whole pipeline.
• Built an end-end architecture and pipeline for artificial intelligence (AI) enabled microservices and plugins for large enterprise products in the Telecom industry.
• Build and deploy Natural Language Understanding (NLU), Data Parsing, Data Warehousing and Document-AI based automated system for delivery challans and invoice parsing & mapping solution. Implemented specialized versions of these solutions for clients in the Oil & Gas industry and Retail market.

Tools & Technologies: Python, Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), GCP (functions, AutoML, storage, ml), AWS (lambda, s3, compute engine, api-gateway), Rasa Conversational AI, Dialogflow (Google AI), Flask, Django, MongoDB, MySQL, Git, Docker

• Working as Independent Contractor with international clients ranging from startups to SMEs with revenue in millions. Im mostly building POCs and MVPs for early stage companies in silicon-valley to help them out with product design layouts, infrastructure and data pipelines. Have worked with more than 15 clients having millions in revenue, through AICodeLab in the last 8+ years.
• Worked as team-lead as well as team-member with companies from a wide range of domains from Fin-Tech, Health-Care, SaaS and E-Commerce business. Helped grow revenue by thousands to millions which was achieved by reducing business costs, optimizing the performances, reducing operations and processing times with cutting edge technologies machine learning (ML), Deep Learning (DL) and natural language processing projects (NLP), cloud/ serverless etc.
• Built and developed multiple teams to support business and service goals while architecting and implementing the application transformation for scalability and better client experiences.
• Designed and implemented the automation pipeline for three statement financial models that resulted in 90% less time required for processing historical data.

Tools & Technologies: Python, Machine Learning (ML), Natural Language Processing (NLP), GCP, AWS, Flask, REST, DevOps, MySQL, Node.js, Typescript, Data Engineering.

• A core member of the startup product development and was involved in design, development, and implementation. Operated through every development and implementation life cycle.
• Involved in SOA integration with other systems in the ed-tech space; used Django, Django-rest-framework for different client systems.
• Infrastructure Management for most of the clients(CI/CD, Cluster, Load balancing).
• Implemented supporting apps for handling courses, assignments, discussions, and quizzes along with a core backend engine.