Open to opportunities

Muhammad Shoaib

@muhammadshoaib5

Senior AI/ML Engineer with over 6 years of experience designing, building, and deploying production-grade Generative AI, LLM systems.

Pakistan

Message

What I'm looking for

I'm looking for a role where I can solve challenging technical problems, contribute to meaningful projects, and work with a collaborative, forward-thinking team. Ideally, I want to be part of a product-driven environment that values clean code, thoughtful architecture, and long-term impact over short-term fixes.

I am a software-focused AI/ML engineer who thrives on the full lifecycle of intelligent systems—from translating ambiguous business problems into fine-tuned models to monitoring secure, auto-scaling production environments. Over the past six years, my work has spanned high-impact solutions across enterprise platforms and healthcare ecosystems.

Currently, as a Senior Machine Learning Engineer at DataOnMatrix, I architect advanced Transformer-based systems using GPT, Llama, and Qwen models, implementing deep learning pipelines in PyTorch and TensorFlow to process massive structured and unstructured datasets. My engineering philosophy centers on reliability, observability, and performance; I specialize in optimizing inference workflows, structuring FastAPI/REST microservices, and managing real-time data streaming layers via Apache Kafka.

I have a proven history of shipping production-ready applications, including an automated Generative AI validation system that processed over 10,000 enterprise records during Azure cloud migrations, and predictive time-series sensor models deployed via AWS SageMaker with real-time Grafana monitoring. Holding a Gold Medal M.S. in Computer Science, I blend strong technical fundamentals in Python, SQL, and cloud infrastructure with an agile, collaborative approach to build software that scales seamlessly.

Core Skills

Generative AI & LLMs: GPT-4, Llama, Qwen, LangChain, Transformer Architectures, Retrieval-Augmented Generation (RAG), Vector Databases, Semantic Search, Model Fine-Tuning & Inference Optimization
Machine Learning & Deep Learning: PyTorch, TensorFlow, Keras, NLP (spaCy, Hugging Face), Supervised/Unsupervised Learning, Time Series Forecasting (LSTM, XGBoost), Feature Engineering
Programming & Backend Engineering: Python (Pandas, NumPy, Scikit-learn), FastAPI, Flask, REST APIs, Microservices Architecture, SQL, R, Jupyter Notebooks, Git
Cloud & MLOps Infrastructure: AWS (SageMaker Pipelines, EC2, S3, Lambda), Azure (Data Factory, Synapse Analytics), GCP, CI/CD, Model Drift Detection, Prometheus, Grafana
Data Engineering & Caching: PostgreSQL, MySQL, MongoDB, Redis, Apache Kafka, ETL Pipelines, Data Pipeline Automation, Apache Spark

Experience

Work history, roles, and key accomplishments

Current

Senior Machine Learning Engineer

Current

DataOnMatrix

Jul 2022 - Present (4 years)

Designed and deployed Transformer-based Generative AI models using GPT, Llama, and Qwen for enterprise and healthcare use cases. Built production deep learning pipelines and fine-tuned LLM workflows with an emphasis on scalability, reliability, and streamlined data preprocessing.

Generative AI LLM GPT LLaMA Qwen PyTorch TensorFlow LLM Fine tuning Machine Learning Pipelines MLOps data preprocessing Documentation Scalability Reliability

AI/ML Engineer

Biafotech DMCC

Jul 2020 - Jul 2022 (2 years)

Built scalable machine learning pipelines for predictive analytics and business intelligence across classification, regression, and clustering use cases. Developed FastAPI REST APIs and AI microservices, and deployed/monitored models on AWS and Azure with retraining and drift-detection workflows.

Predictive Analytics Classification regression Clustering fastAPI REST APIs Microservices Apache Kafka Real Time Data Streaming AWS Azure Model Monitoring Monitoring Drift Agile Scrum

AI/ML Engineer

PAiT

Aug 2019 - Jun 2020 (10 months)

Developed NLP solutions for text processing, analytics, intelligent data extraction, and recommendation systems to improve operational workflows. Built end-to-end ML pipelines for training, validation, deployment, and monitoring, integrating models via REST APIs and microservices architecture.