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Schedule Interview NowMy name is Awais N. and I have over 4 years years of experience in the tech industry. I specialize in the following technologies: Natural Language Processing, Data Science, Deep Learning, Machine Learning, Data Scraping, etc.. I hold a degree in , , , Master's degree. Some of the notable projects I’ve worked on include: Mental Health AI Agents, Forecast Sales using Machine Learning, Data Analysis and Visualization, Fine-tuning LLaMA, AI Multi Classifier Chat Analysis, etc.. I am based in Lahore, Pakistan. I've successfully completed 29 projects while developing at Softaims.
I specialize in architecting and developing scalable, distributed systems that handle high demands and complex information flows. My focus is on building fault-tolerant infrastructure using modern cloud practices and modular patterns. I excel at diagnosing and resolving intricate concurrency and scaling issues across large platforms.
Collaboration is central to my success; I enjoy working with fellow technical experts and product managers to define clear technical roadmaps. This structured approach allows the team at Softaims to consistently deliver high-availability solutions that can easily adapt to exponential growth.
I maintain a proactive approach to security and performance, treating them as integral components of the design process, not as afterthoughts. My ultimate goal is to build the foundational technology that powers client success and innovation.
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This project uses GPT API for mental health counseling by simulating expert opinions from various therapy techniques like CBT, Psychodynamic, and Mindfulness Therapy. Dynamic prompting incorporates topic inputs, few-shot examples, and social determinants of health (SDoH) to generate personalized advice. Counseling responses are evaluated on psychotherapy factors like empathy, engagement, and alliance capacity, ensuring high-quality guidance. This approach combines AI with psychotherapy expertise to deliver tailored mental health support.
In this project, I developed machine learning models to forecast sales using a variety of algorithms, including Linear Regression, Random Forest Regressor, XGBoost Regressor, and LSTM-based reinforcement learning. Trained and evaluated these models using sales data from 10 retail stores, assessing performance with metrics such as mean squared error, mean absolute error, and R2 score. This analysis helps businesses forecast sales trends and make strategic budget decisions while offering flexibility for further optimization.
In this project, I conducted a comprehensive data analysis and visualization of a video game sales dataset. The analysis involved exploring sales trends across various platforms, genres, and regions using Python libraries such as pandas, NumPy, and Matplotlib. The insights gained from this analysis help game publishers and platform developers make informed decisions, particularly highlighting the dominance of North America and Europe in sales and the potential of specific genres in Japan. This project also emphasized the importance of data-driven strategies in the gaming industry.
Fine-tuned LLaMA for Diagnosis-Related Group (DRG) prediction using sequence classification on hospital discharge summaries. The model processes MIMIC-IV clinical data, extracting brief hospital courses to classify them into standardized DRG codes. Implemented LoRA-based fine-tuning on LLaMA-7B, optimizing training efficiency. Integrated a Gradio interface for live inference, enabling seamless DRG prediction. Used preprocessing pipelines to map multi-year DRG data into a unified version.
I developed a multi model NLP pipeline to classify chatbot queries by sentiment, emotion, intent, and topic for an educational platform. And deployed on AWS SageMaker with Airflow triggers, it processes 200+ queries daily in GPU batches and stores results in RDS. I used RoBERTa for sentiment and DeBERTa for zero shot classification. Automated with lifecycle scripts for cost efficiency $5/day, the system provides structured insights for downstream analytics and personalization, with future ready support for real-time and multilingual expansion.
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2013-01-01-2017-01-01
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2011-01-01-2013-01-01
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2003-01-01-2011-01-01
Master's degree in Data Science
2023-01-01-2025-01-01