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AI Engineer with less than a year in Machine Learning, Computer Vision, and Multi-Modal AI.
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Computer Science undergraduate with hands-on experience building ML pipelines, computer vision systems, and multi-modal AI projects. Passionate about research-driven development from reading academic papers to deploying production models. Skilled in Python, TensorFlow/PyTorch-adjacent frameworks, OpenCV, and MediaPipe. Seeking to contribute to Unsiloed-AI's vision of transforming unstructured documents into machine-readable representations through deep learning and multi-modal architectures.
Keshav Memorial Engineering College, Osmania University
B.E. · Computer Science & Engineering
August 1, 2023 – June 30, 2027
Real-Time Customer Churn Alert System
September 1, 2025 – November 30, 2025
Engineered a live streaming ML pipeline via Kafka scoring churn probability in real time using a Random Forest model achieving AUC 0.89. Feature-engineered 15+ behavioural signals (session gap, cart abandonment, login streak decay) lifting F1score +22% over baseline — demonstrating research-driven model improvement. Deployed a Streamlit dashboard with live risk scores & 7-day trend; simulated 50K events end-to-end with full architecture diagram on GitHub.
TALQS - Transformer-based Legal Question Answering & Summarization
September 1, 2025 – June 19, 2026
Built a Transformer-based architecture to parse and summarize complex legal documents — directly aligned with Unsiloed-Al's multi-modal document parsing mission. Fine-tuned pre-trained language models for domain-specific legal Q&A, implementing attention-based extraction of key clauses and entities from unstructured text. Processed and curated custom legal datasets; evaluated model accuracy and robustness across diverse document formats including contracts, FIRs, and case summaries.
Hand Gesture Volume Control System
January 1, 2024 – December 31, 2024
Developed a real-time computer vision system using MediaPipe to detect and track 21 hand landmarks at inference speed - demonstrating vision-first, layout-aware ML model deployment. Engineered a spatial distance metric between finger landmarks to map gesture data to system audio control, showcasing multi-modal input processing from raw visual data. Integrated Pycaw for system-level audio interaction, building a full end-to-end pipeline from camera input → landmark detection → signal processing → system output.
F1 Race Strategy Optimization & Sales Demand Forecasting
January 1, 2024 – December 31, 2025
Built a lap-time analysis system using Pandas to model tire degradation trends; designed a pit-stop optimizer from first principles - benchmarking strategies against performance data. Compared Linear Regression, Random Forest & Gradient Boosting across 18 months / 50+ SKUs; selected RF via cross-validation, boosting accuracy ~18% with lag features and seasonality flags.
Google Data Analytics Professional Certificate
Unknown
June 19, 2026 – Present
Oracle Cloud Infrastructure 2025 Certified Generative AI Professional
Oracle University
October 1, 2025 – Present
Data Analytics Job Simulation
Deloitte / Forage
October 1, 2025 – Present
GenAI Project School
Keshav Memorial Engineering College
September 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects showcase a diverse range of applications within AI, including NLP, computer vision, and real-time analytics. This breadth of interest, coupled with certifications in Generative AI, aligns well with an innovative and research-oriented culture. The focus on 'transforming unstructured documents into machine-readable representations' in their summary directly matches the target company's mission, indicating strong cultural alignment.
Soft Skills & Operational Fit
The candidate demonstrates a research-driven approach and a passion for deploying production models, which suggests a strong operational fit. Their project descriptions indicate an ability to work on complex problems and integrate various technologies. The academic nature of all projects means real-world operational constraints and team collaboration experience are not explicitly demonstrated.