Shuvam Maity

AI Engineer

https://talent.gravityer.com/shuvam-maity

AI Engineer with less than a year in Data Science & Machine Learning

Key Strengths

Demonstrated ability to build end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, and evaluation.
Experience with Retrieval-Augmented Generation (RAG) systems and large language models (LLMs) like Google Gemini, directly relevant to an AI Engineer role.
Proficiency in various machine learning algorithms (XGBoost, Random Forest, LSTM, Prophet, MLP) and deep learning frameworks (TensorFlow, Keras, PyTorch, Hugging Face Transformers).
Strong understanding of NLP concepts, including transformer-based transfer learning, fine-tuning RoBERTa, tokenization, and text classification.
Practical experience with MLOps components like Streamlit for deployment and Git/GitHub for version control.
Solid foundation in statistical analysis and data visualization, crucial for data-driven AI development.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's project portfolio shows a strong interest in diverse AI applications, from RAG systems and NLP to network intrusion detection and time series forecasting. This breadth of interest aligns well with a dynamic AI engineering environment that values continuous learning and exploration of new domains. The academic background and certifications suggest a proactive approach to skill development. However, the limited professional experience means that adaptability to corporate culture and established development workflows is an area that would need further assessment.

Soft Skills & Operational Fit

The candidate's project descriptions indicate a problem-solving mindset, particularly in addressing challenges like class imbalance (SMOTE) and model efficiency (XGBoost vs. others). The deployment of applications on Streamlit Community Cloud suggests an understanding of making solutions accessible. The academic background in Data Science and Statistics provides a strong analytical foundation. However, the lack of professional experience beyond a single internship and the academic nature of most projects mean that operational fit in a fast-paced industry environment, including collaboration within larger teams and handling production-grade systems, is yet to be fully demonstrated.

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Projects

RAG-based PDF Question Answering System

January 1, 2026 – June 1, 2026

Built a Retrieval-Augmented Generation (RAG) application that lets users upload multiple research papers and ask natural-language questions, with answers generated by Google's Gemini LLM grounded strictly in the retrieved document context to minimize hallucination. Implemented full source traceability by displaying the exact source PDF, page number, and paragraph behind every generated answer, and deployed the app publicly on Streamlit Community Cloud for live demo access.

Toxic Content Detection using Fine-Tuned RoBERTa

January 1, 2026 – June 1, 2026

Fine-tuned RoBERTa-base on the Jigsaw Toxic Comment dataset (159K+ comments) to classify toxic and non-toxic online content, achieving 96.7% accuracy and 0.84 F1-score. Developed and evaluated an end-to-end NLP pipeline, improving performance over a TF-IDF + Logistic Regression baseline (F1: 0.745 → 0.840) through transformer-based transfer learning.

AI-Driven Network Intrusion Detection System (NIDS)

January 1, 2025 – December 31, 2025

Built an end-to-end machine learning pipeline that classifies 15 types of cyberattacks within 2.8M records to achieve a 99.95% Recall rate and a 0.99 F1-score by implementing SMOTE to resolve extreme class imbalances. Carried out a comparative analysis of XGBoost, RF, and Deep Learning (MLP) models, with the dimensionality of the features decreased by 30% using correlation analysis, and concluded that the production model of choice is indeed XGBoost for the remarkable 10x improvement in training efficiency.

Certifications

Presented a paper on Snowball Sampling in Statosphere

Statosphere

June 1, 2026 – Present

Deloitte job simulation experience involving data analysis and forensic technology

Deloitte

June 1, 2026 – Present

MS Excel Specialization on Coursera

Coursera

June 1, 2026 – Present

Key Strengths

Demonstrated ability to build end-to-end machine learning pipelines, including data preprocessing, feature engineering, model training, and evaluation.
Experience with Retrieval-Augmented Generation (RAG) systems and large language models (LLMs) like Google Gemini, directly relevant to an AI Engineer role.
Proficiency in various machine learning algorithms (XGBoost, Random Forest, LSTM, Prophet, MLP) and deep learning frameworks (TensorFlow, Keras, PyTorch, Hugging Face Transformers).
Strong understanding of NLP concepts, including transformer-based transfer learning, fine-tuning RoBERTa, tokenization, and text classification.
Practical experience with MLOps components like Streamlit for deployment and Git/GitHub for version control.
Solid foundation in statistical analysis and data visualization, crucial for data-driven AI development.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Shuvam Maity

Key Strengths

Cultural & Operational Fit

About

Top Skills

Skills

Education

Experience

Projects

Certifications

Key Strengths

Cultural & Operational Fit