Machine Learning Engineer | Applied AI & Cloud

Liam McAllister

Professional Summary

AI/ML engineer with expertise in building scalable multi-agent systems, retrieval-augmented search, and cloud-based AI pipelines. Skilled at integrating research innovations into production environments, delivering measurable improvements in workflow efficiency and model performance.

Work Experience

AI/ML Engineer at PrairieMind Labs

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• Designed and deployed multi-agent AI systems for document tutoring, retrieval-augmented chatbots, and autonomous research assistants. • Developed persona-based agents for collaborative code planning, improving research workflow efficiency by 40%. • Architected scalable AI pipelines integrating Pinecone vector databases and LangChain for real-time retrieval and search.

Machine Learning Intern at SnowPeak AI

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• Built YouTube thumbnail click-through prediction models using DistilBERT and ResNet, improving accuracy by 15%. • Fine-tuned large language models with GRPO for enhanced instruction-following and alignment. • Optimized GCP inference pipelines, reducing latency by 30% for AI services.

Education

Bachelor of Science in Computer Science

University of Calgary

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Skills

Technical Skills

  • Python
  • JavaScript
  • SQL
  • Transformers
  • LangChain
  • Pinecone
  • Vertex AI
  • Hugging Face
  • Vision Transformers
  • ResNet
  • DistilBERT
  • GRPO fine-tuning
  • Google Cloud Platform
  • FastAPI
  • Docker

Soft Skills

  • Collaborative problem solving
  • Research-driven innovation
  • Effective communication
  • Time management
ATS Score
85/100
Skills
82/100
Projects
78/100
Education
75/100
Profile
88/100
Work Experience
87/100
Certifications
0/100
Header
90/100
Add certifications relevant to AI/ML or cloud computing to enhance credibility and ATS keyword density.
Standardize date formats in work experience and projects for better ATS parsing and recruiter clarity.
Include start dates and GPA in education to improve completeness and ATS compatibility.
Add more explicit keywords related to MLOps, model deployment, and machine learning lifecycle in profile and skills sections.
Expand projects section with dates, technologies used, and links to demonstrate practical impact and accessibility.
Demonstrated impact through quantifiable achievements in AI/ML projects and work experience.
Strong technical proficiency in cutting-edge AI frameworks and cloud platforms.
Clear and concise professional summary and profile highlights that emphasize value to employers.
Good balance of hard and soft skills relevant to AI engineering roles.

Skills

82%
Comprehensive hard skills list covering programming languages, ML frameworks, and cloud platforms, supporting strong ATS keyword presence.
Soft skills and tools are appropriately separated, demonstrating well-rounded capabilities.
To improve, add a concise skills summary section at the top or incorporate skill proficiency levels (e.g., expert, intermediate) to aid recruiter evaluation.

Projects

78%
Projects are described with clear technical focus and outcomes, showcasing practical application of skills.
Inclusion of multi-agent AI and multi-modal models aligns well with current AI trends.
Adding dates and specific technologies used per project would improve ATS recognition and recruiter understanding.
Consider linking to project repositories or demos if available.

Education

75%
Education section includes degree, institution, and relevant coursework which supports domain knowledge.
Missing start date and GPA might reduce completeness; adding these can improve ATS parsing and recruiter confidence.
Consider listing any academic projects or honors to further strengthen this section.

Profile

88%
Strong summary clearly communicates expertise in AI/ML and cloud technologies with quantifiable achievements, which is attractive to both ATS and recruiters.
Profile highlights contain impactful metrics and specific technologies, reinforcing the candidate's value proposition.
To optimize further, incorporate more industry-specific keywords such as 'model deployment', 'machine learning lifecycle', or 'MLOps' to improve ATS keyword matching.

Work Experience

87%
Descriptions use bullet points with quantifiable impact metrics (e.g., 40% efficiency improvement, 15% accuracy gain), which demonstrates results-oriented experience.
Technical tools and methodologies are well integrated into the descriptions, enhancing keyword richness.
Include consistent date formatting (month and year) for start and end dates to improve clarity and ATS parsing.
Consider briefly mentioning team size or cross-functional collaboration to highlight interpersonal skills.

Certifications

0%
Section missing

Header

90%
Header includes full name, professional headline with relevant keywords, and multiple contact methods including LinkedIn and GitHub links, which improves ATS and recruiter accessibility.
Consider adding a complete address or at least a postal code to enhance location specificity for local job searches.