SOFTWARE ENGINEER
AI / ML
PYTORCH
DATA PIPELINES
CLOUD ML
I build applied AI and machine learning systems — from raw data and feature engineering to trained models, evaluation, and deployment-ready prototypes.
My experience spans computer vision, forecasting, data analysis, and full ML pipelines, with a strong emphasis on writing code that is measurable, debuggable, and useful beyond notebooks.
I care about clean data flows, correct experimental design, and turning models into tools people can actually interact with.
| AREA | WHAT I WORK ON | WHY IT MATTERS |
|---|---|---|
| Applied AI / ML | Model training, evaluation, experimentation | Correct decisions need reliable models |
| Data Pipelines | Ingestion, preprocessing, feature engineering | Garbage in = garbage out |
| Computer Vision | Detection, segmentation, spatial & image-based models | High-impact real-world applications |
| Forecasting & Time Series | Time-aware modeling, indicators, evaluation | Most demos get this wrong |
| Tooling & Visualization | Dashboards, analysis tools, model inspection | Visibility beats blind accuracy |
| Systems & Integration | APIs, auth, cloud services, deployment | Models must live in systems |
AI / ML
PyTorch · TensorFlow/Keras · Deep Learning · Model Evaluation · Feature Engineering
DATA & ANALYSIS
Pandas · NumPy · Time Series · Visualization · APIs / Scraping
BACKEND / FULL-STACK
Node.js · Express · React · REST APIs · Auth (JWT / OAuth)
CLOUD / DEVOPS
Azure · AWS · basic GCP · Docker · Kubernetes · CI/CD exposure
DATABASES
MongoDB · MySQL · SQL Server
AI / ML internships or early-career roles where I can work on real datasets, end-to-end pipelines, and applied machine learning problems — especially in environments that value correct evaluation and production-minded thinking.
[AI / ML] END-TO-END ML PIPELINES & EXPERIMENTATION
WHAT IT DOES
Designs and evaluates machine learning pipelines covering data ingestion, preprocessing, training, and evaluation.
TECH STACK
Python · PyTorch · Feature Engineering · Evaluation Metrics
WHY IT’S INTERESTING
Most ML issues come from pipeline mistakes, not model choice.
OUTCOME
Reproducible experiments with clear performance comparisons and reliable results.
[CV] INDUSTRIAL OBJECT DETECTION — CLOUD BENCHMARKING
WHAT IT DOES
Benchmarks cloud-based object detection systems using a shared industrial dataset.
TECH STACK
Azure Custom Vision · Google Vertex AI · Roboflow · Python · Dashboard UI
WHY IT’S INTERESTING
Real deployments care about iteration speed, dataset handling, and cost — not just accuracy.
OUTCOME
Identified the most practical platform based on accuracy, efficiency, and workflow fit.
[ML] NEURAL FORECASTING & TIME-SERIES ANALYSIS
WHAT IT DOES
Forecasts next-day stock prices using neural models with proper time-aware validation.
TECH STACK
Python · Neural Networks · Technical Indicators · Streamlit
WHY IT’S INTERESTING
Many forecasting demos leak future data — this pipeline avoids that entirely.
OUTCOME
Interactive dashboard comparing models, error metrics, and trading simulations.
[FULL-STACK / WEB] APMC JUDGING PLATFORM
WHAT IT DOES
A complete web-based judging platform where users can submit entries, reviewers can evaluate them, and results are processed and displayed through a structured workflow.
TECH STACK
React · Node.js · Backend APIs · Database · Auth · System Design
WHY IT’S INTERESTING
Judging platforms are state-heavy systems — handling submissions, roles, evaluation logic, and result consistency requires careful backend design, not just UI work.
OUTCOME
Fully functional end-to-end system with a clean frontend, reliable backend logic, and well-defined judging workflows.
[FULL-STACK] PURRRFECT MATCH — SYSTEM DESIGN & INTEGRATION
WHAT IT DOES
Full-stack web platform with authentication, media handling, and backend services.
TECH STACK
MongoDB · Express · React · Node.js · OAuth · JWT · Cloudinary · PostMark
WHY IT’S INTERESTING
Demonstrates how ML systems eventually fit into real applications.
OUTCOME
Production-style app with clean UX and robust backend integration.
If you’re working on AI, machine learning, data, or applied systems, I’m always open to conversations — internships, collaborations, or technical discussions.
Mian Adnan Khaliq