AI & ML Expertise
Building production-ready deep learning models and scalable ML pipelines.
Hi, I'm
Bilal Shafiq
I am
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AI and Machine Learning Engineer with 9+ years of experience building production-ready intelligent systems and scalable data platforms. Specialized in MLOps, deep learning, and workflow automation using n8n, delivering end-to-end AI solutions from data ingestion to deployment.
Technical Expertise
Skills & Technologies
Expertise in AI, Machine Learning, Data Engineering, and scalable cloud-based systems
Deep Learning0%
TensorFlow0%
Natural Language Processing0%
Supervised Learning0%
Unsupervised Learning0%
Reinforcement Learning0%
Feature Engineering0%
Model Evaluation0%
Apache Spark0%
PySpark0%
Dask0%
ETL / ELT Pipelines0%
Airflow0%
Data Warehousing0%
Snowflake0%
End-to-End ML Pipelines0%
CI/CD for ML0%
Model Deployment0%
Model Monitoring0%
Batch & Real-Time Serving0%
Production ML Architecture0%
n8n Automation0%
API Integration0%
LLM Integration0%
AI Workflow Automation0%
REST APIs0%
AWS0%
Azure0%
Docker0%
Git & GitHub0%
Cloud Deployment0%
Python0%
SQL0%
NoSQL0%
Flask0%
Django0%
Profile
About Me
AI & Machine Learning Engineer with 9+ years of experience building scalable systems, data pipelines, and intelligent automation solutions that solve real-world problems.
Data Engineering
Designing high-performance pipelines using Spark, Airflow, and modern data stacks.
Automation & AI Workflows
Automating workflows using n8n and APIs to reduce manual effort.
Cloud & Deployment
Deploying scalable AI systems on AWS & Azure with reliability.
MLOps & CI/CD
End-to-end ML pipelines with automated retraining, monitoring, and CI/CD integration.
Production-Grade Systems
Building reliable, scalable, and maintainable AI solutions for real-world business impact.
Experience
Professional Journey
Lead Data Engineer
TechnioX Systems- Designed and implemented end-to-end scalable data pipelines using Apache Spark, PySpark, and Dask, reducing batch processing time by 50%
- Built cloud-native data architectures on AWS and Azure, integrating Snowflake, Data Lakes, and OLAP systems
- Developed production-ready ML pipelines with CI/CD workflows, automating model retraining, deployment, and version control
- Led n8n workflow automation for AI-driven reporting and ETL orchestration, improving operational efficiency
- Established data governance and quality monitoring frameworks to ensure data integrity and compliance
- Optimized cloud resource usage and automated scaling strategies, reducing infrastructure costs while maintaining performance
Senior Data Scientist
Bilsmark Inc- Developed supervised and unsupervised ML models for predictive analytics, improving forecasting accuracy by 35%
- Built end-to-end MLOps pipelines with batch and real-time serving, automated retraining, monitoring, and CI/CD integration
- Implemented NLP and deep learning solutions for chatbots, document analysis, and recommendation systems
- Integrated AI workflows via APIs and n8n, reducing manual reporting and repetitive tasks by 60%
- Mentored junior data scientists on feature engineering, model evaluation, and production-grade deployment
- Designed and maintained data pipelines, preprocessing frameworks, and analytics dashboards
Junior AI/ML Developer
Systems Limited- Developed ML algorithms from scratch for classification, regression, and clustering projects in finance and healthcare
- Designed data preprocessing and transformation pipelines for structured and unstructured datasets
- Implemented containerized ML solutions using Docker and deployed pipelines on cloud platforms
- Collaborated with teams to integrate ML models into Flask and Django web applications
- Built automated reporting and workflow systems, improving productivity by 40%
- Conducted model evaluation, feature engineering, and hyperparameter tuning to optimize performance
AI Engineer
Freelance- Delivered AI-powered applications and ML models for SMBs in healthcare, education, and logistics
- Built end-to-end ML pipelines, including deployment, monitoring, and versioning
- Automated workflows using n8n and Python scripts, reducing operational overhead by 50%
- Conducted data analysis, feature engineering, and model optimization for client-specific AI solutions
- Provided consulting on cloud deployment strategies, data architecture, and scalable ML practices
- Developed dashboards to track ML model performance, insights, and operational metrics
Projects
Key Highlights of My Work
Production-ready AI, ML, and Data Engineering projects from GitHub portfolio.
View All Projects on GitHubHealthcare Management System
Python-based platform for managing patient data, appointments, and analytics, improving operational efficiency in healthcare workflows.
View ProjectAI Voice Assistant
TypeScript-powered AI assistant capable of handling commands, automation tasks, and API integrations.
View ProjectFake Job Posting Prediction
Jupyter Notebook ML model to detect fraudulent job listings, improving accuracy and trust in recruitment platforms.
View ProjectHouse Price Prediction WebApp
Machine learning web application for real estate price forecasting, delivering actionable insights for buyers and agents.
View ProjectDepression Data Analysis
Conducted mental health data analysis using ML techniques, generating insights to support healthcare interventions.
View ProjectCustomer Segmentation (ML)
Implemented clustering and profiling techniques to segment customers, enabling targeted marketing and personalization.
View ProjectWind Power Forecasting
Built predictive models for renewable energy generation, supporting smarter energy management and planning.
View ProjectEnd-to-End MLOps Pipeline Template
Reusable production-ready ML pipeline template, including CI/CD, monitoring, and automated retraining.
View ProjectHomemade Machine Learning (From Scratch)
Implemented ML algorithms from scratch in Jupyter Notebook to demonstrate fundamental concepts and custom solutions.
View ProjectDiabetes Prediction Using ML
Classification model for predicting diabetes risk using healthcare datasets, supporting early intervention strategies.
View ProjectBatch Serving ML Pipeline
Scalable batch inference pipeline in Python for handling large-scale model predictions efficiently.
View Project
Education
Background & Qualifications
Stanford University
Bachelor of Science in Information Technology
2010 — 2014
- Specialization in Artificial Intelligence and Machine Learning
- Projects on AI pipelines, deep learning, and workflow automation
- Focused on cloud deployment, scalable data platforms, and MLOps practices
Professional Certifications
AI & Machine Learning, Cloud, and Automation
Various Dates
- Certified in MLOps and scalable data engineering pipelines
- n8n Automation Engineer Training
- Cloud Certifications on AWS & Azure
Contact
Let's Connect
Location
San Francisco, California, United States
Lahore, Pakistan