About Me
AWS Machine Learning Associate Certified AI/ML Engineer specializing in deploying production LLM systems and scalable ML infrastructure using Amazon Bedrock and SageMaker. Proven expertise in prompt engineering, RAG architectures, and multi-model LLM orchestration with hands-on experience achieving 60% efficiency gains and 92% accuracy in production AI systems. Strong background in ML governance, model auditability, and end-to-end deployment of compliant, enterprise-grade AI solutions. Published researcher ready to drive ML innovation at technology companies.
Publications
FilletCam AI: Precision fillet color profiling using deep learning. Journal of Agriculture and Food Research (2024).
What’s unique: First real-time edge deployment for aquaculture color grading (92% accuracy).
DOI: 10.1016/j.jafr.2024.101461
Mosquito identification using ML on embedded systems. IEEE ITU Kaleidoscope Conference (2021).
What’s unique: Embedded ML on ARM Cortex-M; low-latency (
<100 ms) inference with high accuracy.
DOI: 10.23919/ITUK53220.2021.9662116
For Recruiters
- Open to: Machine Learning Engineer, Production ML Systems, LLM Applications
- Relocation: Open to relocation
- Start date: Immediately available
Technical Skills
Machine Learning & AI
Generative AI, LLMs, RAG, LangChain, PyTorch, TensorFlow, Computer Vision, YOLOv8, OpenCV, NLP, Scikit-learn, Keras
Deep Learning Specializations
Neural Networks, CNNs, RNNs, Transformers, Fine-tuning, RLHF, Anomaly Detection, Reinforcement Learning, Time Series
Software Engineering
Python, C++, Java, SQL, FastAPI, Flask, Git, Docker, Kubernetes, CI/CD, API Development, Microservices, Test-Driven Development
Cloud & MLOps
AWS (Bedrock, SageMaker, Lambda, Textract, EC2, S3), MLflow, Weights & Biases, Model Monitoring, Model Governance, A/B Testing, Auditability
Data Engineering
Pandas, NumPy, Spark, Hadoop, Feature Engineering, Data Pipelines, ETL, PostgreSQL, MongoDB, Redis
Deployment & Tools
Streamlit, Gradio, Jupyter, CUDA, Edge Computing, NVIDIA Jetson, Model Serving, Performance Optimization
Core Skills Proficiency
Based on production experience and project implementations
Machine Learning & AI
Cloud & MLOps
Programming & Engineering
Data Engineering
Professional Experience
AI/ML Researcher
UMBC Center for Real-time Distributed Sensing and Autonomy
- Led 5-engineer team developing distributed ML systems for multi-modal sensor data fusion under US Army Research Lab funding, achieving 20% performance improvement in real-time optimization systems.
- Designed production monitoring dashboards and real-time inference pipelines enabling autonomous systems deployment with computer vision and sensor fusion capabilities. Demo Video
- Engineered production-ready multi-modal ML systems integrating advanced perception algorithms for next-generation AI applications using deep learning frameworks and edge computing platforms.
- Built CHARLIE Voice Assistant, a voice-enabled AI agent integrating speech recognition, LLM inference, and real-time dialogue management for autonomous system interfaces.
ML Engineer - Production AI Systems (Contract)
VITG Corp., Halethorpe, MD
- Architected and deployed production LLM automation system using AWS Bedrock and SageMaker, leveraging Claude 3.5 and Llama models to reduce candidate screening time by 60% for 200+ employee organization, processing 2,500+ regulatory documents with 40% efficiency improvement.
- Implemented RAG-based conversational AI chatbot with retrieval-augmented generation architecture using AWS Textract for document ingestion, vector embeddings for semantic search, and prompt-engineered LLM responses, achieving enterprise-grade compliance and auditability for regulatory workflows.
- Established ML governance framework and model monitoring pipelines ensuring compliance, auditability, and performance tracking across deployed LLM systems with automated logging, versioning, and evaluation metrics.
- Developed scalable ETL pipelines and RESTful APIs for real-time geospatial data processing and multi-model LLM inference, enabling seamless integration with enterprise systems.
Data Scientist - Computer Vision
The Conservation Fund, Shepherdstown, WV
- Developed production-ready computer vision system achieving 92% accuracy using YOLOv8 and OpenCV, deployed on edge devices for real-time quality assessment with end-to-end ML pipeline from data collection to model serving.
- Co-authored peer-reviewed research demonstrating commercial impact of deep learning applications, enabling commercial deployment of AI quality control systems with automated reporting capabilities.
Education
Master's in Data Science
University of Maryland Baltimore County (UMBC)
GPA: 3.8/4.0
Relevant Coursework: Natural Language Processing, Data Analysis & Visualization, Data Management, Big Data Processing, Financial Data Science, Machine Learning, Artificial Intelligence
Bachelor's in Electronics & Communications Engineering
Gujarat Technological University
GPA: 3.8/4.0
Relevant Coursework: Machine Learning & AI, Python Programming, Internet of Things, Embedded Systems
Key Projects
Enterprise Document Intelligence Platform
AWS, Textract, S3, Lambda, Python, LLM APIs, Streamlit
- Built scalable document processing system automating extraction from 2,500+ PDFs using LLM-powered OCR pipelines and cloud infrastructure with real-time API endpoints and automated compliance workflows.
- Implemented interactive dashboards enabling stakeholders to query and visualize insights from unstructured data with automated reporting and decision-support capabilities.
Multi-Modal Recommendation Engine
Python, OpenAI API, Computer Vision, PostgreSQL, A/B Testing
- Engineered hybrid recommendation system combining structured data analysis with computer vision, reducing user bounce rate by 20% and increasing engagement by 40% through personalized AI-driven recommendations.
- Implemented scalable data pipelines and A/B testing framework for continuous model improvement and performance monitoring with real-time analytics and user feedback integration.
AI-Powered Medical Diagnosis System
Python, TensorFlow, Computer Vision, Flask, Gradio
- Designed end-to-end ML web application for medical image classification achieving high diagnostic accuracy using deep learning and computer vision techniques with automated feature extraction.
- Deployed production-ready inference pipeline with interactive web interface, enabling real-time medical assessment and automated reporting capabilities for healthcare applications.
Real-Time Analytics Dashboard
Python, Dash, Machine Learning, Statistical Analysis, Plotly
- Built interactive ML-powered analytics platform with real-time data ingestion, model inference, and visualization capabilities for performance optimization and predictive insights.
- Integrated predictive analytics improving performance outcomes by 20% through data-driven insights, automated reporting, and statistical modeling techniques.
Gesture-Based Control System
Python, OpenCV, Real-time Processing, Edge Computing, NVIDIA Jetson
- Developed real-time gesture recognition system using computer vision and machine learning for intuitive device control and human-computer interaction with low-latency inference.
- Implemented efficient edge computing solution enabling responsive control in resource-constrained environments with optimized model deployment on embedded hardware.
Beyond Work
Robotics
Exploring how robots can assist in everyday tasks and specialized environments.
Generative AI
Experimenting with the latest LLMs and generative models to create new possibilities.
Outdoor Activities
Hiking and exploring nature to find balance and inspiration.
Continuous Learning
Always seeking new knowledge and skills in emerging technologies.
Achievements & Certifications
AWS Machine Learning Foundations
Core ML concepts & AWS integrations
NVIDIA Deep Learning Institute
Transformer-Based NLP, GPU Computing
2 Published Research Papers
IEEE & Journal of Agriculture