ATS Score
85/100
Skills
88/100
Certifications
75/100
Education
85/100
Projects
90/100
Header
90/100
Profile
85/100
Work Experience
80/100
Convert work experience descriptions into bullet points with consistent formatting to improve readability and ATS parsing.
Add issuing authorities and award dates to certifications to enhance credibility and ATS detection.
Include more detailed technology stacks and methodologies in work experience to increase relevant keyword density.
Incorporate links to projects, code repositories, or demos to provide tangible proof of skills.
Expand the summary with specific quantifiable achievements to capture recruiter attention quickly.
Strong technical expertise in AI/ML, NLP, LLMs, and computer vision demonstrated through education, projects, and work experience.
Proven leadership and mentoring skills with experience managing teams and guiding junior members.
Robust portfolio of impactful projects with quantifiable results and advanced technical implementations.
Comprehensive skill set including cloud services (AWS), MLOps, and multiple programming and data tools.
Skills
88%
A strong mix of hard skills, soft skills, and tools is presented, covering relevant AI/ML technologies and cloud platforms.
Consider grouping skills by category (e.g., Programming Languages, Cloud Platforms, Frameworks) to enhance scannability.
Add keywords related to 'model versioning', 'CI/CD pipelines', or 'data engineering' to align with MLOps roles.
Certifications
75%
Certifications listed are relevant but lack issuing authority and award dates, which are important for validation.
Consider adding more certifications in cloud, AI, or security domains to strengthen professional credibility.
Formatting certifications with full details and standardized naming improves ATS recognition.
Education
85%
Advanced degrees from reputable institutions with good GPAs are clearly listed, supporting technical expertise.
Including relevant coursework, thesis topics, or academic projects could provide additional context for recruiters and ATS.
Consistent date formatting is good; adding honors or distinctions would further highlight achievements.
Projects
90%
Projects are highly relevant, technically detailed, and include performance metrics and outcomes, which is excellent for both ATS and human readers.
Including links to project repositories or demos would enhance credibility and recruiter engagement.
Header
90%
The header includes a clear, keyword-rich headline that highlights key skills and certifications, which enhances ATS matching.
Including a professional LinkedIn URL is a plus; consider adding a personal portfolio or GitHub link to showcase projects.
Profile
85%
The summary is concise and well-tailored to AI/ML roles, emphasizing relevant experience and technical domains.
Including quantifiable achievements or specific metrics in the summary could further strengthen impact.
Profile highlights effectively summarize core competencies; consider integrating keywords like 'model deployment' or 'data pipeline optimization' for ATS.
Work Experience
80%
Descriptions include leadership and technical accomplishments with some quantifiable results, aiding recruiter appeal.
Expand on specific technologies and methodologies used in each role to improve keyword density for ATS.
Use consistent bullet points instead of paragraphs to improve readability and ATS parsing.
Add exact dates (month/year) consistently to all roles for clarity.