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AI Engineer with less than a year in ML Engineering & Data Analytics
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Data Scientist and ML Engineer skilled in statistical modeling, feature selection, and A/B testing with experience delivering ML systems across data pipelines, ETL workflows, model training, and cloud deployment. Proficient in Python, SQL, Scikit-learn, TensorFlow, PyTorch, MLflow, and AWS with strong MLOps practices including CI/CD, model monitoring, drift detection, and Docker.
National Institute of Technology, Bhopal (NIT Bhopal)
Bachelor of Technology · Civil Engineering
August 1, 2021 – June 30, 2025
Secondary Education
Class X
N/A – May 31, 2018
Senior Secondary Education
Class XII
N/A – May 31, 2020
SOBHA Limited
Graduate Engineer Trainee, Data and Project Analytics
September 1, 2025 – February 1, 2026
Dubai, Dubai, United Arab Emirates
Network Security Intrusion Detection System
January 1, 2024 – Present
Developed a modular ML pipeline on the CICIDS2017 dataset (958,000 rows) for network intrusion detection, achieving 99.93% accuracy, 99.91% F1-score, and 100% ROC-AUC using XGBoost. Designed a 5-stage pipeline (ingestion, validation, transformation, training, anomaly detection); applied feature selection to reduce dimensions from 100 to 40 via correlation filtering, variance thresholding, and RF importance; used SMOTE to balance classes and improve model stability. Developed a PyTorch Autoencoder for unsupervised anomaly detection and integrated SHAP TreeExplainer for per-prediction attribution; tracked experiments with MLflow and DagsHub and deployed a live Streamlit dashboard with predictions and SHAP visualizations.
Research Paper Q&A System using RAG
January 1, 2024 – Present
Built a Retrieval-Augmented Generation (RAG) pipeline processing uploaded PDFs with semantic chunking to generate context-aware answers via the Groq LLM API. Implemented dense vector embeddings with Sentence Transformers (all-MiniLM-L6-v2) generating 384-dimensional chunk representations; integrated FAISS for sub-linear similarity search using L2 distance indexing. Designed a modular object-oriented architecture with custom exception handling, structured logging, and production safeguards; deployed on Hugging Face Spaces as a live Streamlit application.
View ProjectCultural Fit Analysis
The candidate's project diversity, ranging from network security to RAG systems, indicates a broad interest and adaptability, which is a good cultural fit for dynamic AI roles. Participation in Kaggle and competitive programming suggests a proactive learning attitude and a drive for continuous improvement. The target role of AI Engineer aligns well with the candidate's demonstrated technical skills and project experience in ML/DL and MLOps.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through competitive programming and Kaggle participation. Project descriptions indicate an ability to design modular architectures and implement production safeguards, suggesting attention to detail and operational robustness. The experience at SOBHA Limited, though not directly AI-focused, shows an ability to automate processes, analyze data, and deliver actionable insights to stakeholders, which are valuable operational skills.