AI Engineer with 4+ years in Machine Learning & Applied ML Systems
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Final-year engineering student specializing in machine learning and applied ML systems, with hands-on experience building data-driven models, scalable pipelines, and evaluation workflows using Python and industry-standard ML libraries.
International Institute of Information Technology (IIIT)
B.Tech · Electronics and Telecommunication Engineering
August 1, 2022 – June 30, 2025
DAV Public School, Unit-8
12th
June 1, 2019 – May 31, 2021
DAV Public School, Unit-8
10th
June 1, 2019 – May 31, 2019
Deep Matrix
Data Engineer Intern
January 1, 2022 – Present
Bhubaneshwar, Odisha, India
Research Evolution Analyzer
January 1, 2024 – Present
Built an AI-powered research intelligence pipeline using Python and the Semantic Scholar Graph API to fetch, validate, and analyze publication metadata, with local caching and retry handling for reliable data ingestion. Generated semantic embeddings using SentenceTransformers (all-MiniLM-L6-v2) and applied topic modeling with BERTopic to identify semantically related research themes and analyze topic evolution over time using linear regression trends. Developed a metadata-aware semantic retrieval system using FAISS IndexFlatL2 for similarity search, along with collaboration intelligence workflows based on co-authorship frequency, citation impact, and publication timelines.
YouTube Chatbot (RAG-based System)
January 1, 2024 – Present
Built a RAG-based chatbot that allows real-time interaction with YouTube videos (Q&A, summarization, doubt resolution). Engineered pipeline with LangChain, OpenAI LLMs, FAISS, and YouTube Transcript API, incorporating transcript loading, recursive text splitting, embeddings, semantic retrieval, and context-augmented generation. Utilized Python, Transformers, OpenAI Embeddings, and vector DB integration; explored deployment with Streamlit and Chrome extension.
HR Recruitment & Performance Analytics System (ML-based)
January 1, 2023 – Present
Built an end-to-end HR recruitment and performance analytics system using machine learning, covering data preprocessing, model training, and deployment. Applied data cleaning, feature scaling, and class balancing, and trained multiple classifiers including Logistic Regression, Random Forest, SVM, K-NN, and Naive Bayes, selecting the best model using accuracy and confusion matrices. Integrated the final ML model with a Flask-based web application and analytics dashboard to enable interactive predictions and data-driven insights.
Cultural Fit Analysis
The candidate's personal projects showcase a strong initiative and passion for AI/ML, aligning well with an innovative and learning-oriented culture. The diversity of projects (research analysis, RAG chatbot, HR analytics) indicates a broad interest in applying AI to different domains. The internship experience at 'Deep Matrix' suggests an ability to work in a professional ML environment. However, the lack of diverse team projects or open-source contributions beyond personal projects limits the assessment of broader cultural fit.
Soft Skills & Operational Fit
The candidate demonstrates analytical thinking, problem-solving, communication, and team collaboration skills through project descriptions and internship experience. The ability to design and implement scalable ML models and collaborate with cross-functional teams indicates a good operational fit for an AI Engineer role.