Machine Learning Engineer | Full-Stack Developer | AI-Powered Product Builder

Mike Doe

Professional Summary

Machine Learning Engineer with hands-on experience developing AI-powered solutions and full-stack applications. Skilled in designing, training, and deploying machine learning models using Python, TensorFlow, PyTorch, and cloud services such as AWS and Azure. Proven ability to deliver scalable, data-driven products and optimize ML pipelines to support business objectives in agile environments.

Work Experience

Data Scientist Intern at Agriculture & Agri-Food Canada

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• Led preprocessing of large geospatial datasets using pandas, NumPy, and SQL, reducing ETL time by 30%. • Conducted exploratory data analysis and statistical testing to identify predictors of crop yield. • Engineered predictive features from satellite imagery and remote sensing data to improve model quality. • Trained XGBoost and Logistic Regression models to forecast soil risk and yield with 85% accuracy. • Used cross-validation and stratified sampling across 12 geographies to enhance generalization. • Built ML pipelines in MLflow with version tracking and performance comparison. • Deployed models on AWS Lambda and S3 using Docker containers. • Created interactive dashboards in Streamlit to support real-time decision-making. • Worked in Agile sprints using GitHub Projects and Trello.

Machine Learning Engineer Intern at Generico AI Startup

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• Built ML-powered features for a cloud analytics platform with over 1000 active users. • Trained and integrated YOLOv5 for backend image detection with 92% accuracy. • Developed RESTful APIs using NestJS to deliver image classification and snapshot management. • Improved API speed and reliability by 25% through Postman and Swagger-driven testing. • Created OCR pipeline using TensorFlow and PyTorch for claims documents with 86% accuracy. • Integrated microservices with Microsoft Dynamics ERP and deployed to Azure.

Skills

Technical Skills

  • Machine Learning
  • AI
  • Python
  • scikit-learn
  • XGBoost
  • PyTorch
  • TensorFlow
  • MLflow
  • Prophet
  • ARIMA
  • LangChain
  • GPT-4
  • Claude
  • Pandas
  • NumPy
  • SQL
  • MongoDB
  • GDAL
  • GeoPandas
  • Streamlit
  • PowerBI
  • Plotly
  • Seaborn
  • Matplotlib
  • FastAPI
  • NestJS
  • Flask
  • Express
  • OpenAI API
  • Postman
  • Swagger
  • ReactJS
  • NodeJS
  • Tailwind CSS
  • Framer Motion
  • MERN Stack
  • Particles.js
  • AWS Lambda
  • AWS S3
  • Azure
  • Docker
  • GitHub Actions
  • CI/CD
  • Firebase
  • Kubernetes (basic)
  • Terraform (basic)
  • PyTest
  • JWT
  • OAuth2 (basic)
Score
78/100
Work Experience
75/100
Header
85/100
Profile
80/100
Projects
78/100
Education
0/100
Certifications
0/100
Skills
80/100
Add education details to provide foundational background and improve ATS completeness.
Include relevant certifications to validate skills and improve recruiter confidence.
Provide a phone number and fully qualified URLs in the header for better ATS and recruiter accessibility.
Standardize date and employment type formatting in work experience to enhance ATS parsing.
Incorporate soft skills and group technical skills by category for improved readability.
Strong technical expertise in machine learning frameworks and cloud deployment (TensorFlow, PyTorch, AWS, Azure).
Demonstrated ability to deliver measurable impact with quantifiable results in internships and projects.
Experience building end-to-end ML pipelines with version tracking and deployment.
Clear focus on AI-powered product development and full-stack capabilities, showing versatile skill set.

Work Experience

75%
Descriptions are detailed with strong use of action verbs and quantifiable results (e.g., 'reduced ETL time by 30%', 'achieving 92% accuracy').
Use consistent formatting for dates and employment type fields; currently, these fields are empty or inconsistent which may reduce ATS parsing accuracy.
Consider adding a brief company description or industry context for each employer to enhance recruiter understanding.
Separate technical skills/tools used per role in a distinct section or bulleted format to improve readability.

Header

85%
The header includes a clear professional headline with relevant keywords such as 'Machine Learning Engineer' and 'Full-Stack Developer' which improves ATS matching.
Add a phone number to enhance recruiter contact options and include a full LinkedIn URL (with https://) for better ATS parsing.

Profile

80%
The summary is concise and includes strong keywords relevant to machine learning and cloud technologies, aiding ATS and recruiter appeal.
Consider adding a brief objective or career goal to clarify candidate aspirations and tailor the profile to specific roles.
Include a few quantifiable achievements or metrics in the summary to immediately showcase impact.

Projects

78%
Projects are well described with clear technical details and outcomes, demonstrating practical application of skills.
Add technologies used for each project explicitly to improve keyword density and ATS recognition.
Include links to project repositories or demos if available to provide recruiters with direct evidence of work.

Education

0%
Section missing. Include education details such as degree, institution, dates, and relevant coursework to strengthen the resume.

Certifications

0%
Section missing. Adding relevant certifications (e.g., AWS Certified Machine Learning, TensorFlow Developer Certificate) would boost credibility and ATS ranking.

Skills

80%
The skills section is comprehensive, listing many relevant hard skills and tools, which is excellent for ATS keyword matching.
Add a soft skills subsection to demonstrate interpersonal and teamwork abilities valued by employers.
Group skills by category (e.g., Programming Languages, Frameworks, Cloud Platforms) for better clarity and scanning.