> ./init.sh
Ramin Sharifi
Transforming cutting-edge AI concepts into production-ready solutions.
Building intelligent systems at the intersection of ML, software engineering, and autonomous agents.
// About Me
The intersection of research and engineering
Senior Machine Learning Engineer with deep expertise in time-series modeling, IoT data pipelines, and applied deep learning. I specialize in transforming complex AI research into production-grade systems that operate at scale.
My work spans sequential prediction using LSTM/GRU architectures, anomaly detection for sensor-driven systems, and large-scale data engineering pipelines deployed on cloud platforms. I've published in IEEE conferences and led teams building multi-agent AI systems.
$ MASc @ UVic # 2021
$ BSc @ IUST # 2018
// Experience
Building production ML systems and pushing research boundaries
Turing Company
Senior ML Engineer / Team Lead
- ▸Designed real-time data processing pipelines for anomaly detection and forecasting in large-scale autonomous systems
- ▸Built scalable backend services using FastAPI, Celery, and PostgreSQL for high-frequency data ingestion
- ▸Architected multi-agent AI systems integrating predictive signals and cross-agent coordination
- ▸Developed full-stack dashboards in React.js/Next.js for visualizing time-series telemetry and live metrics
- ▸Led cross-functional engineering efforts integrating ML-powered analytics into production systems
Hummingbird Drones
Machine Learning Engineer
- ▸Built end-to-end IoT data pipelines for high-frequency sensor streams using AWS/GCP
- ▸Designed ML models for sequential pattern analysis, forecasting, and anomaly detection
- ▸Optimized ML inference pipelines for near-real-time operation
Kinsol Research
Data Scientist
- ▸Utilized YOLO V7 for object detection with automated annotation workflows
- ▸Integrated Hidden Markov Models into ML pipelines for sequential data analysis
- ▸Applied data mining techniques for business intelligence at scale
University of Victoria
Research Assistant
- ▸Conducted research in capsule networks for image classification and object detection
- ▸Optimized models using pruning techniques, reducing computational costs
4M Biotech
Technical Analyst
- ▸Developed iOS apps with on-device ML inference using Swift and CoreML
- ▸Designed the Gelderm classifier for improved medical image classification
// Tech Stack
Tools and technologies I work with daily
// Publications
Peer-reviewed research in machine learning and neural networks
Pruning in Capsule Networks: A Survey
R. Sharifi, P. Shiri, A. Baniasadi
Quick-CapsNet (QCN): A Fast Alternative to Capsule Networks
P. Shiri, R. Sharifi, A. Baniasadi
Zero-skipping in CapsNet: Is it Worth It?
R. Sharifi, P. Shiri, A. Baniasadi
Mobile User-Activity Prediction Utilizing LSTM Recurrent Neural Network
R. Sharifi, M. M. Majdabadi, V. Tabataba Vakili
// Projects
Selected work across ML systems, agentic AI, and full-stack development
Agentic AI Playground
2025Modular AI agents using Dify, LangChain, and CrewAI for multi-step reasoning and workflow automation. Integrated observability, vector search, and dynamic tool orchestration.
LangChain & CrewAI Pipelines
2025Reusable agent pipelines for software engineering automation — code refactoring, repository organization, and documentation synthesis.
Real-Time IoT Telemetry Dashboard
2023End-to-end pipeline for processing high-frequency sensor streams with live visualization of time-series data, anomaly detection alerts, and predictive analytics.
Gelderm Classifier
2021Mobile-optimized computer vision system for medical image classification. Separated detection and classification phases for improved accuracy with on-device inference via CoreML.
LSTM Activity Predictor
2019LSTM-based forecasting model for mobile user-activity prediction. Published at IEEE PACRIM 2019 — demonstrated sequential pattern analysis for real-world mobile data.
// Get in Touch
Let's build something intelligent together
const contact = {
role: "Senior Software Engineer / Team Lead @ Turing",
email: "raminsharifi@proton.me",
location: "Toronto, Ontario, Canada",
}