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AI Engineer with less than a year in Machine Learning & Generative AI
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring AI/ML Engineer with strong foundation in Python, Machine Learning, and Generative AI. Seeking to design and deploy intelligent solutions using LLMs, RAG pipelines, and backend integration to deliver scalable AI-powered applications.
Shri Vaishnav Institute of Information Technology
B.Tech · Computer Science & Engineering
August 1, 2022 – June 30, 2026
IBM PBEL Internship (MeitY & NASSCOM)
AI/ML Intern (Remote)
January 1, 2024 – March 1, 2024
India
The Internspire
Software / IT Intern (Indore, MP)
October 1, 2023 – December 1, 2023
Indore, Madhya Pradesh, India
AI Dynamic Resume Builder
June 1, 2026 – Present
Automated resume generation system using Python logic, rule-based structuring, and NLP.
Blockchain Ledger System
June 1, 2026 – Present
Developed secure ledger using hashing for transaction integrity.
Student Performance Prediction
June 1, 2026 – Present
Implemented supervised learning algorithms to predict student outcomes.
DBMS & Cloud Computing
NPTEL
June 1, 2026 – Present
Introduction to Python
IBM
June 1, 2026 – Present
Microservices Architecture and Implementation
IBM
June 1, 2026 – Present
Cybersecurity Essentials
Cisco
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity includes AI/ML, NLP, and a blockchain system, indicating a broad interest in different technical domains. The target role of 'AI Engineer' aligns well with the candidate's stated objective and recent internship focus. However, the experience level (0 years) and academic nature of most projects suggest a need for significant mentorship and ramp-up in a professional setting, which might impact immediate cultural integration into a senior-level team.
Soft Skills & Operational Fit
The candidate lists problem-solving, team collaboration, and adaptability as soft skills. The internship experiences, particularly at IBM PBEL, suggest exposure to collaborative technical environments. However, the short duration of internships and academic nature of projects limit the depth of evidence for these skills in a professional operational context.