AI/ML Engineer
Data Science and Machine Learning Engineer with experience building applied AI systems for healthcare and research. Strong focus on EEG signal analysis, computer vision, time series modeling, and ML driven clinical insights. Skilled in designing end to end pipelines for biomedical data processing, feature extraction, model development, and result interpretation. Hands on experience with deep learning, LLM based report generation, seizure detection research, and movement analysis for neurological conditions. Technical expertise includes Python, PyTorch, TensorFlow, scikit learn, OpenCV, MediaPipe, YOLO, Pandas, NumPy, and MNE. Experienced in computer vision, NLP, transformers, LSTM models, and statistical data analysis. Interested in privacy aware AI, secure ML systems for healthcare, and translating complex models into interpretable, real world solutions.
U: The Mind Company
1 yr 1 mo · United States
U: The Mind Company
1 yr 9 mos
-Developed an end-to-end EEG analysis pipeline for preprocessing and analyzing EEG signals, including artifact removal. Extracted key features to uncover neurological patterns, detect seizure onsets, and support clinical diagnostics. -Built a prompt-engineered script to interpret EEG data and automatically generate comprehensive, human-readable EEG reports by incorporating domain-specific medical context and terminology. -Enhanced and customized MediaPipe and YOLO models to improve accuracy in hand landmark detection, supporting automated tremor assessment and Parkinson’s disease detection from video data. -Designed and implemented a tremor data analysis pipeline, applying advanced preprocessing techniques to reduce noise. Analyzed tremor amplitude and Power Spectral Density (PSD) to quantify movement disorder severity and evaluate treatment effectiveness. -Performed comprehensive feature extraction and statistical analysis on EEG and tremor data, capturing frequency domain dynamics and temporal patterns to inform clinical decision-making and research studies. -Collaborated closely with neurologists and healthcare professionals to validate analysis pipelines and ML models, ensuring outputs are clinically relevant, interpretable, and applicable to real-world healthcare scenarios. - Designed and developed an AI-powered research peer review system that automatically evaluates academic papers across multiple dimensions including novelty, methodology, clarity, citation quality, ethics, and completeness. -Built an automated LLM-based review pipeline capable of generating structured reviewer reports, scoring papers, and producing recommendation decisions similar to academic conference review formats. -Integrated research knowledge sources and NLP techniques to support literature comparison and novelty detection. -Built an automated pipeline to analyze research documents and generate structured patent-style document
DREaD Co-Innovation Network Foundation
1 yr 10 mos · India
Disruptive Research Engineering Assisted Design Private Limited
10 mos · India
SouvenirCart
4 mos · Hyderabad, Telangana, India
Virtusa
6 mos · Hyderabad, Telangana, India
U: The Mind Company
Aug 2023 – Present · USA · Remote
Architected and deployed n8n-based agentic automation pipelines for patent prior art search and academic peer review. Built a 7-agent parallel Claude reviewer pipeline with GROBID parsing, automated scoring, structured JSON outputs, and retry handling. Designed an AI-powered real-time EEG diagnostic assistant with GPT-4o and Node.js that achieved 85% concordance with neurologist assessments. Engineered computer vision pipelines for Parkinson's disease tremor quantification, improving hand landmark detection accuracy by 25% in a 20-patient clinical trial. Built full-stack ML inference pipelines with Python, PostgreSQL/Supabase, Docker, and AWS. Achieved 95% clinical relevance through iterative model tuning and stakeholder validation, and created SOPs and training materials for non-technical users.
DREaD Co-innovation Network
Aug 2023 – Present · Remote
Built and optimized machine learning models for medical imaging and symptom pattern recognition, reducing false positives by 22%. Designed reproducible preprocessing and evaluation workflows for structured healthcare datasets and documented model performance for peer-reviewed research publication.
Minor, Artificial Intelligence
Aug 2024 - May 2025
Bachelor of Engineering - BE, Computer Science
2019 - 2023
Minor, Artificial Intelligence
Aug 2024 – Oct 2025
Coursework included Deep Learning, Computer Vision, and NLP.
B.E., Computer Science
Aug 2019 – Jul 2023
GPA: 3.2 / 4.0
Built a modular n8n automation framework using Claude Code, Anthropic SDK, Supabase, and Slack API for hiring, content review, reporting, and knowledge management. Integrated webhook document classification and routing, stakeholder notifications, and SOPs for non-technical users.
Production-deployed agentic pipeline using webhooks, GROBID, section formatting, classification, seven parallel Claude reviewer agents, score aggregation, Supabase/PostgreSQL, Docker, and Ubuntu/Linux VPS.
Built a multi-step Anthropic Claude SDK and Node.js CLI tool that decomposes product goals, scores priorities on a weighted 1–10,000 scale, and generates Cursor-ready implementation plans with GitHub API integration.
Built an AI-powered hiring workflow for application ingestion, Claude-based resume scoring and ranking, Google Sheets output, and automated Gmail outreach. Reduced manual screening time by approximately 80%.
Designed an n8n workflow using Claude, Notion API, Google Docs API, and Supabase to repurpose long-form content into social posts, summaries, and SEO briefs, with human approval and audit trail.
Created Node.js and Python REST API workflows using GPT-4o to parse multi-format EEG data and generate structured clinical reports with validation guardrails and WebSocket-ready delivery.
Built a Python analytics pipeline using MediaPipe, YOLO, Butterworth filtering, PSD analysis, and Pandas for tremor quantification and treatment efficacy reporting, achieving 92% accuracy across eight movement biomarkers.
Built an AI tutoring chatbot using OpenAI GPT-4o, LangChain, and Python to classify and distill complex topics into Easy, Medium, and Hard structured knowledge pathways.