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AI Engineer with less than a year in Machine Learning & GenAI
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Bhaskar Mondal is an aspiring AI Engineer with 9 months of internship experience focused on Machine Learning and Generative AI. He has hands-on experience in designing and deploying deep learning models, building real-time AI pipelines for audio transcription and NLP, and developing RAG pipelines for technical Q&A. His expertise spans PyTorch, TensorFlow, LangChain, and MLOps practices, contributing to efficient and scalable AI solutions.
Mumbai University
Bachelor of Engineering · Computer Engineering
August 1, 2020 – June 30, 2024
Arcitech AI
AI Engineer Intern
July 1, 2025 – December 1, 2025
Mumbai, Maharashtra, India
Ebizon Cloud
AI Engineer Intern
March 1, 2025 – May 1, 2025
Cheyenne, Wyoming, United States
BareMetalML
May 1, 2026 – Present
Built an open-source machine learning library from scratch using only NumPy (no scikit-learn), implementing core ML algorithms end-to-end to gain first-principles understanding of underlying mathematics and optimization. Implemented Linear Regression via Normal Equation, Batch/Stochastic Gradient Descent, and Ridge (L2) Regularization, validated on real-world regression datasets with custom MSE/R2 metrics. Implemented Logistic Regression (Sigmoid activation, Binary Cross-Entropy loss) supporting binary and multiclass classification via One-vs-Rest (OvR), and built Decision Trees with Gini Impurity/Entropy-based recursive splitting and decision boundary visualization. Currently extending to ensemble learning – implementing Random Forest via Bootstrap Aggregation (Bagging) (in progress); designed a modular, testable package architecture with reusable utilities (train-test split, StandardScaler, MinMaxScaler, evaluation metrics) and Git feature-branch workflow.
View ProjectCultural Fit Analysis
The candidate's diverse project portfolio, ranging from foundational ML library development to advanced GenAI and real-time NLP systems, indicates a broad interest and adaptability in the AI domain. Their participation in Kaggle competitions and open-source contributions (BareMetalML) suggest a proactive, learning-oriented mindset and a passion for the field. The experience in both startup (Arcitech AI) and remote (Ebizon Cloud) environments, coupled with leading technical decisions, points to a self-starter who can thrive in dynamic settings. This aligns well with a culture that values innovation, continuous learning, and impactful contributions.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and a first-principles approach to learning, as evidenced by the BareMetalML project. Their experience leading AI R&D and MLOps decisions in an intern role suggests a proactive and responsible work attitude. The project descriptions indicate an ability to work independently and collaborate effectively with cross-functional teams. The focus on optimizing models for constraints and ensuring compliance (PII redaction) highlights attention to detail and practical application of AI solutions.