The future runs on AI.
Meet the engineer building it.
AI / Machine Learning Engineer — building production-ready AI systems with Python, Deep Learning, Agentic AI, and FastAPI, transforming ML models into scalable, deployed solutions.
The future runs on AI.
AI / Machine Learning Engineer — building production-ready AI systems with Python, Deep Learning, Agentic AI, and FastAPI, transforming ML models into scalable, deployed solutions.
For the last three years I've been closing the gap between AI theory and systems that actually run. That has meant more than seven hands-on projects — moving from supervised and unsupervised learning experiments to deployed implementations with real evaluation behind them.
I care about the parts that don't show up in a demo: cutting false negatives on a clinical model, lifting recall on the minority class, guarding a pipeline with CI so it doesn't quietly break. Alongside the code, I've led a team to a live robotics demo, represented my university on climate, and ranked #2 on my institution's GeeksforGeeks leaderboard.
The work is practical, the discipline is real, and I'm just getting started.
From designing neural networks to shaping raw data into features and shipping with an engineer's discipline.
Designing, training and tuning models end to end — from artificial neural networks to classic supervised and unsupervised methods, with careful evaluation at every step.
Turning raw, messy data into model-ready features and building language pipelines that score, rank and match text at scale.
Shipping with an engineer's discipline — version control, reproducible experiments, model versioning and prompt engineering for practical AI workflows.
Syed M. Jahanzaib Khalid is an AI / Machine Learning Engineer based in Bahawalpur, Pakistan. His journey in technology is built around continuous learning, experimentation, and the belief that intelligent systems can help solve real-world challenges.
Over the past three years, he has explored and developed AI solutions across machine learning, deep learning, natural language processing, and MLOps. His work includes seven-plus hands-on projects, ranging from predictive models and AI-powered applications to production-oriented systems. Notable projects include a heart-disease prediction neural network achieving 95.12% accuracy across 1,026 patient samples, customer-churn prediction models, an NLP-based resume analyzer, and an AI-powered security-log analyzer featuring a live SIEM dashboard and CI/CD pipeline.
Beyond technical development, Jahanzaib actively contributes to leadership and community initiatives. He led the human-following robot project as Team Lead at AI Expo IUB 2024 and holds the #2 institutional rank on GeeksforGeeks with 160+ problems solved. He serves as a climate ambassador at LCOY, Joint Secretary of the IUB Digital Media Society, and an executive member of the AI Student Club, combining technology with collaboration, communication, and social impact.
Outside of technology, he values curiosity, nature, learning, and connecting with people. Whether building AI systems, contributing to communities, or exploring new ideas, his goal remains the same: to keep learning, create meaningful solutions, and use technology to make a positive difference.
Six builds that moved from notebook to something you can actually run.
Artificial neural network that predicts heart disease risk from clinical data across 1,026 patient samples — preprocessing, feature scaling and evaluation tuned to cut false negatives.
Python · TensorFlow · Keras · ANNDeep learning classifier trained on a 10,000-record dataset — engineered features and tuned thresholds to lift recall on the minority churn class.
Python · TensorFlow · Keras · ClassificationNLP pipeline using TF-IDF vectorization and cosine similarity to score and rank resumes against job descriptions — automating a fully manual screening task.
Python · NLP · TF-IDF · Cosine SimilarityEnd-to-end MLOps and cybersecurity pipeline — an AI classification model performs live and batch anomaly detection on security logs, analyzed in real time through a modern, responsive SIEM dashboard. Lifespan-managed FastAPI backend, bulk CSV ingestion, guarded by a GitHub Actions CI test pipeline.
Python · Scikit-learn · FastAPI · GitHub Actions CIRandom Forest classifier predicting wine quality from physicochemical properties, with feature analysis and rigorous model evaluation.
Python · Scikit-learn · Random ForestAn AI-powered price predictor helping freelancers and clients estimate fair project pricing — machine learning analyzes project requirements, complexity and real-time market trends. Ships with a dynamic web interface and an automated GitHub Actions CI/CD pipeline.
Python · Scikit-learn · FastAPI · GitHub Actions CI/CDWhere the work meets people — teams, stages and communities.
Have a role, a project or an idea that needs a model behind it? I'd love to hear about it.