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Ai engineer
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer responsible for designing, developing, and deploying AI/ML solutions, including machine learning models and generative AI applications. Skilled in Python, cloud platforms, data processing, model optimization, and integrating AI systems into production environments to automate processes and solve business problems.
Skn Sinhgad Institute Of Technology & Science
BE · computer
June 14, 2021 – June 17, 2025
AWS
coursera
June 10, 2026 – June 11, 2036
The candidate scored 94% on the 'Data Scientist — Artificial Intelligence' exam, indicating a very strong grasp of the subject matter and related skills.
Strengths
Limitations
Cultural Fit Analysis
The candidate's project experience, particularly the 'Ad-Maker Using AI-ML' project, demonstrates an innovative and problem-solving mindset, which is a good fit for an AI Engineer role. The 'Employee Payroll System' project, while not directly AI-related, shows versatility in full-stack development and CI/CD practices. The candidate is currently pursuing a Bachelor's degree, indicating a strong academic foundation and a continuous learning attitude. The AWS certification further supports a proactive approach to skill development. The breadth of skills across frontend, backend, databases, and tools suggests adaptability and a willingness to engage with diverse technical challenges.
Soft Skills & Operational Fit
The psychometric test score of 329/500 suggests moderate performance in logical reasoning, work attitude, stress handling, and team collaboration. While the resume mentions 'Leadership, Teamwork, Effective Communication' as soft skills, the psychometric test indicates potential areas for development in these operational aspects. The English test score of 60/100 suggests average communication clarity and professional language usage.