Co-Founder & CTO at TechnoFreaks and Senior Python Developer / Data Scientist at SignDevOps. Engineering live cloud systems, automated ETL pipelines, and predictive ML models.
Based in Karachi, Pakistan. I specialize in bridging the gap between raw data chaos and clean, automated decision systems.
Writing robust, production scripts with Selenium, BeautifulSoup, Requests, and Flask. Built scheduled daemons reducing manual reporting by 40%.
Designing normalized MySQL schemas, writing advanced SQL queries (window functions, subqueries), and building multi-source ETL pipelines.
Feature engineering, outlier isolation, and training Scikit-learn models (Lasso, Random Forest, Logistic Regression) evaluated on ROC-AUC and F1-Score.
Constructed Pakistan Stock Exchange live price trackers, MetaTrader 5 order execution bots, and vectorized backtesting suites.
From raw web endpoints to production ML inference and executive intelligence.
Autonomous scraping daemons extracting structured records from dynamic sites with retry logic and error logging.
Multi-format ingestion (CSV, JSON, XLS) with automated type casting, missing value imputation, and duplicate removal.
Exploratory data analysis uncovering distribution skewness, correlation heatmaps, and multivariate outlier isolation.
Supervised regression & classification algorithms trained with cross-validation and hyperparameter optimization.
Interactive client dashboards, portfolio trackers, and automated PDF export reports for non-technical stakeholders.
Lightweight REST endpoints delivering sub-20ms inference predictions and cron-driven background execution.
Verified open-source repositories and client data platforms.
High-availability enterprise agency platform and digital software ecosystem. Directing end-to-end technical architecture, containerized Docker microservices, Nginx reverse proxies, SSL/TLS automation, and multi-subdomain live deployments.
Clinical-grade deep learning model for 4-class Diabetic Retinopathy screening with CLAHE enhancement, calibrated confidence scoring, and Grad-CAM visual heatmaps.
Real-time Pakistan Stock Exchange monitoring suite collecting live tickers, storing historical market data in MySQL, and rendering interactive Plotly charts.
Automated web scraping daemon extracting live job listings from dynamic portals with schema deduplication, retry logic, and MySQL batch loads.
Supervised regression ML model with location-based one-hot encoding, outlier clipping, and a sub-20ms inference Flask microservice.
Proven track record delivering client automation and data engineering solutions.
Rigorous foundation in Algorithms, Data Structures, Relational Database Management Systems (MySQL), Linear Algebra, Probability, and Software Architecture.
Feedback from engineering leaders across Pakistan and global markets.
"Majid built our automated PSX stock data scraper and MySQL storage pipeline. It handles market spikes smoothly with zero downtime. Exceptional Python skills."
"The quantitative backtesting engine and MetaTrader 5 bot Majid developed gave us complete clarity on our edge and daily risk management."
"Majid engineered our low-latency price alert daemon and real-time WebSocket feed. Clean modular Python code and proactive communication."
Cross-discipline engineers and cloud practitioners I actively collaborate with on production systems and developer tooling.
For enterprise cloud architecture, CI/CD automation, and Kubernetes production deployments, collaborate with Sikander Ali Kumbhar (DevOps & DevSecOps Engineer & Founder/CTO at TechnoFreaks).
Useful developer utilities and formatting tools available at Naveed's DevOps Tools. Essential converters, code formatters, and network utilities for rapid engineering iterations.
Direct access to all 26 production engineering articles across MLOps, explainable computer vision, high-concurrency Python backends, algorithmic trading, and system design.
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In university coursework and Kaggle competitions, a machine learning project ends triumphantly with model.fit() and a test accuracy metric. In commercial engineering, model.fit() is merely the first 15% of the software lifecycle. Here is what actually happens when you must serve 5,000 requests per minute with strict 50ms latency guarantees.
Sculpted neural networks with 98% test precision are useless if a transient database timeout or malformed payload crashes the service. Discover the engineering principles behind the 80% of software that keeps machine learning systems operating 24/7.
Deploying code updates to a standard web app is easy; deploying updates to a machine learning container where cold starts require 12 seconds to load 1.2 GB of neural weights into memory without dropping client requests requires a disciplined blue-green strategy.
Monolithic Python files containing 2,000 lines of mixed pandas data munging, PyTorch models, matplotlib plotting, and Flask routes are impossible to test, audit, or deploy. Here is the clean modular repository architecture that scales.
Traditional software either works or crashes with an exception. Machine learning models fail silently: they accept input, return output with high confidence, and silently destroy business value as underlying real-world patterns change.
Black-box neural networks cannot be trusted in clinical decision support without transparent auditability. Explore how Grad-CAM extracts gradients from the final convolutional feature maps to generate visual heatmaps pinpointing microaneurysms and exudates in retinal photography.
Garbage in, garbage out is especially fatal in diagnostic AI. Passing an out-of-focus or severely underexposed retinal fundus photo into a classifier yields deceptive results. Here is the automated gatekeeper pipeline that ensures only diagnostically viable images reach inference.
Deconstruct the calculus behind modern explainability. Learn why CAM requires global average pooling, how Grad-CAM generalizes to arbitrary CNN architectures, and how to prevent visual artifacts when rendering heatmaps.
When a deep convolutional network outputs 0.98 confidence on an ambiguous diagnostic image, it does not mean there is a 98% probability the classification is correct. Explore how modern architectures lost calibration and how temperature scaling restores statistical trust.
Building machine learning systems for healthcare requires a fundamentally different mindset. Automation is not the goal—empowering human clinicians, eliminating single-point-of-failure risks, and maintaining total auditability are the true engineering imperatives.
Running a script with nohup python script.py & is the easiest way to ensure silent failure at 3:00 AM. Discover how to architect battle-tested Python daemons with proper UNIX signal handling, systemd watchdogs, and dead-man switch heartbeats.
Take a messy 300-line automation script with hardcoded credentials and zero retries, and watch it transform into a 12-factor production microservice with structured logging, exponential backoff, and dependency injection.
Web users expect HTTP responses in under 200 milliseconds. If your endpoint triggers a 45-second PDF export or a multi-minute model inference, you need an asynchronous job queue. Master FastAPI and Celery orchestration.
Web scraping is fundamentally engineering against an adversarial environment. Websites update their DOM classes, change API contracts, and deploy anti-bot protections. Here is how we build ETL scrapers that persist without human intervention.
Headless browser automation is notoriously resource-heavy. We benchmarked Playwright against Selenium over 500,000 automated page evaluations. Here is why modern data pipelines are abandoning WebDriver.
Off-the-shelf backtesting libraries frequently conceal look-ahead bias and unrealistic execution assumptions. Here is the step-by-step engineering breakdown of building a high-speed vectorized simulation engine with realistic friction modeling.
A strategy that boasts a Sharpe ratio of 3.8 and a 72% win rate on historical data almost certainly contains a subtle programming flaw. Learn to diagnose the five fatal errors that transform paper profits into live losses.
Bridging the gap between mathematical models in Python and live order routing in MetaTrader 5 requires robust socket handling, transaction reconciliation, and defensive position sizing.
Retail marketing obsesses over win rate. Institutional quantitative systems focus entirely on mathematical expectancy and asymmetric risk-reward distributions.
Finding an edge is only half the battle. How you size your positions determines whether that edge compounds your portfolio exponentially or leads to mathematical ruin.
Building digital platforms for smaller cities (like Badin and interior Sindh) introduces unique constraints: low-end Android smartphones, patchy 3G/4G connectivity, and cash-on-delivery economics. Here is how we architected Sahulat to meet those challenges.
Uber spends hundreds of millions on H3 hexagonal spatial indexing and distributed Kafka clusters. Learn how to implement sub-100ms driver matching and distance calculations using open-source tools on a single VPS.
In cash-on-delivery ecosystems, your software must function as an immutable financial ledger tracking physical currency handoffs between customers, riders, dispatchers, and merchants.
At TechnoFreaks, we host agency platforms, medical AI portals (RetinaScan, IDRP), super-apps (Sahulat), and client web systems. Here is our modular infrastructure blueprint for managing multiple production domains securely on budget-optimized hardware.
Building software for traditional business owners requires empathy, extreme simplicity, and designing for chaotic real-world environments like hot kitchen order lines and noisy retail counters.
Available for full-time engineering roles, data analytics contracts, and custom automation projects.