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AI Engineer with less than a year in Machine Learning & MLOps
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Assessing your cultural and operational fit
AI enthusiast and a B.Tech student specializing in Computer Science and Engineering (Artificial Intelligence), with a strong academic record (CGPA: 9.02). Possessing practical experience in developing AI-powered solutions, MLOps pipelines, and deep learning models. Proficient in Python, Machine Learning, Deep Learning, NLP, LLMs, and cloud platforms like AWS, Docker, and Kubernetes. Seeking to apply technical skills and passion for AI in an impactful engineering role.
Vishwakarma Institute of Information Technology, Pune
B.Tech · Computer Science and Engineering (Artificial Intelligence)
August 1, 2023 – June 30, 2027
UST Global
Analog Automation – AI-Powered Analog Circuit Design System
January 1, 2026 – April 1, 2026
India
WEC, VIIT Pune
Joint Logistic Head
January 1, 2023 – January 1, 2024
India
End-to-End MLOps Pipeline for Sentiment Analysis
June 1, 2026 – June 1, 2026
Built an end-to-end MLOps pipeline for sentiment analysis using TF-IDF, SVM, DVC, MLflow, Docker, and Kubernetes. Achieved 90.45% accuracy, 91.13% recall and 90.15% Precision on the IMDB dataset through hyperparameter optimization and feature engineering. Deployed a containerized inference service on AWS (S3, EC2, EKS) with automated CI/CD pipelines using GitHub Actions and monitoring via Prometheus.
View ProjectInterviewPrep AI – Multi-Agent Interview Preparation System
June 1, 2026 – June 1, 2026
Developed an AI-powered interview preparation platform using LangGraph, LangChain, FastAPI, LLMs, and Supabase. Built multi-agent workflows for resume parsing, job description matching, adaptive question generation, interview evaluation, and feedback summarization. Designed modular orchestration pipelines with router-based control flow and REST APIs for scalable interview simulation.
View ProjectEngagement and Attention Analysis using Deep Learning
June 1, 2026 – June 1, 2026
Developed a video classification pipeline using the DAiSEE dataset for multi-class engagement prediction. Extracted ResNet50-based features from video frames and modeled temporal dependencies using LSTM and GRU networks. Applied Bayesian hyperparameter optimization and oversampling, achieving 78% validation accuracy, 71% test accuracy, and 0.83 macro F1-score. Accepted and published in IEEE conference proceedings.
View ProjectMachine Learning Specialization
Coursera (Andrew Ng)
June 1, 2026 – Present
Agentic AI with LangChain and LangGraph
Unknown
June 1, 2026 – Present
HackerRank SQL (Intermediate)
HackerRank
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a strong interest and initiative in AI/ML, aligning well with an AI Engineer role. The diversity of projects, from deep learning for video analysis to MLOps pipelines and multi-agent LLM systems, indicates a broad technical curiosity and willingness to explore different facets of AI. The academic publication and certifications further demonstrate a commitment to continuous learning and professional development, which are positive indicators for cultural fit in a dynamic technical environment. The internship experience at UST Global also shows exposure to industry-level projects.
Soft Skills & Operational Fit
The candidate demonstrates good organizational skills through their 'Joint Logistic Head' role, suggesting an ability to coordinate and collaborate. The project descriptions are clear and detailed, indicating good communication of technical concepts. The diversity of projects (academic, personal, industry internship) suggests adaptability and a proactive learning attitude. However, without specific psychometric or English test scores, a deeper assessment of work attitude, stress handling, and team collaboration is not possible.