$ whoami

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Machine Learning Engineer

$ location

📍 Toronto, ON

$ status --current

Building production ML pipeline architecture
Optimizing model deployment workflows
Building fun side projects

$ skills --primary

PythonMicroservicesMachine LearningBackend developmentREST APIs

$ uptime

0y 00m 00d|00:00:00
0 days online

Work Experience

Machine Learning Engineer

Bell Canada

Sept 2022 – Present

Full-time | Toronto, ON

Developed and deployed machine learning models for Bell's Virtual Repair service, achieving 57% resolution rate and reducing customer support call volumes

Key Achievements:

  • Engineered ML-powered customer service automation reducing support calls by 700K annually
  • Led enterprise cloud migration achieving 10x performance gains and 99% uptime
  • Built scalable production APIs serving 1,800+ daily users with comprehensive monitoring
PythonFastAPIGCPBentoMLPySparkOpenShiftBigQueryRedisPostgreSQLDockerGitLabJenkinsNiFiKafkaPowerBIGrafana

Side Projects

Internal Dev Tool

Bell Canada

Streamlit dashboard to eliminate repetitive SQL queries and API calls with ML model performance analysis

Key Features:

  • Built Streamlit dashboard automating database operations, saving team 1 hour daily
  • Created ML model performance analysis interface with state visualization
  • Achieved full team adoption streamlining debugging and evaluation workflows

Highlights:

Full team adoption1 hour daily time savingsML performance visualization

Technologies:

PythonStreamlitPostgreSQLRedisMachine Learning

Apex Tracker Discord Bot

July 2022

Feature-rich Discord bot with slash commands, integrating multiple APIs for game database functionality

Key Features:

  • Developed feature-rich Discord bot with slash commands
  • Integrated RESTful API connections to fetch and display game database information
  • Implemented structured data formatting for user-friendly display
  • Containerized application with Docker for efficient deployment
  • Built support for multiple concurrent instances
  • Interactive command interface for enhanced user experience

Highlights:

Multi-instance supportReal-time data fetchingContainerized deployment

Technologies:

PythonDockerDiscord APISQLRedisREST APIs

IMDB Movies Data Analysis

August 2022

Comprehensive exploratory data analysis and data cleansing on large 10GB+ dataset with machine learning implementation

Key Features:

  • Conducted comprehensive exploratory data analysis on 10GB+ movie dataset
  • Implemented data cleaning and preprocessing techniques
  • Built Decision Tree Classifier with hyperparameter tuning
  • Achieved improved model accuracy through systematic optimization
  • Utilized statistical analysis and data visualization to identify patterns and outliers
  • Created detailed documentation and analysis reports

Highlights:

10GB+ dataset processingML model optimizationStatistical analysis

Technologies:

PythonJupyter NotebooksPandasNumPyScikit-LearnMatplotlib

Technical Skills & Achievements

Programming Languages & Frameworks

Core Languages

PythonJavaJavaScriptSQL

Frameworks & Libraries

FastAPIBentoMLPySparkPandasScikit-Learn

Cloud & Infrastructure

Cloud Platforms

Google Cloud PlatformOpenShiftDocker

DevOps & Tools

JenkinsGitLabApache NiFiGrafana

$ contact --info

Let's Connect

$ location --current

Toronto, ON

$ contact --email

haris.ahmad.cs@gmail.com

$ contact --phone

$ cat resume.pdf

View Resume

$ ls ./social-links

$ echo "message" | mail haris

haris@portfolio:~$
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