Applied AI • Machine Learning • GenAI Systems
Building real-world AI systems,not just models.
I’m Rahul Awale, an Applied AI / Machine Learning Engineer focused on building end-to-end systems - from data pipelines and models to APIs, retrieval systems, and production deployment.
Toronto, ON
Applied AI / ML Engineer
Focused on machine learning systems, RAG pipelines, semantic search, and deployable backend services.
Focus
ML + GenAI
Strength
End-to-End Systems
Featured Work
Selected Projects
A selection of projects across retrieval-augmented generation, OCR-based document intelligence, machine learning APIs, and forecasting workflows.
CivicAI
A full-stack AI-powered municipal by-law assistant that combines RAG, pgvector semantic retrieval, Redis and Celery background processing, S3-based document storage, and FastAPI backend services to deliver structured, citation-backed answers through a production-style architecture.
Belleville By-Law Assistant
An OCR-powered RAG assistant for municipal by-law documents built with Tesseract, MiniLM embeddings, FAISS retrieval, FastAPI, and Streamlit/Gradio interfaces, designed to turn scanned PDFs into citation-grounded legal question answering workflows.
Car Price Prediction
An end-to-end machine learning project covering exploratory analysis, feature engineering, model comparison, and deployment as a FastAPI microservice. Random Forest achieved the strongest performance with R² of 0.908.
Stock Price Prediction: ARIMA vs LSTM
A time-series forecasting project on Tesla stock data comparing ARIMA and LSTM models. LSTM significantly outperformed ARIMA, reducing RMSE from about 43.25 to 13.19 on the evaluated series.
What I Build
End-to-end systems across ML, retrieval, and deployment
My work sits at the intersection of machine learning, applied AI, backend engineering, and production-oriented system design.
Machine Learning Systems
Building practical ML workflows from data preparation and feature engineering to training, evaluation, and inference.
GenAI / RAG Systems
Designing retrieval-based AI applications that combine document ingestion, embeddings, search, and grounded response generation.
Deployment & APIs
Turning models and AI workflows into usable products with API services, containers, async jobs, and cloud-ready architecture.
Experience Snapshot
Software engineering foundation, applied to AI systems
My background in application development helps me approach machine learning work with a stronger focus on APIs, usability, maintainability, and end-to-end delivery.
Software Developer
Aalaya Soft-tech Pvt. Ltd., Nepal
Developed and maintained Flutter applications integrated with REST APIs and analytics modules.
Collaborated with backend teams to design APIs and implement data-driven features.
Mentored interns, reducing bug backlog by 20% and improving delivery speed by 20%.
Built production systems with a strong focus on performance, maintainability, and user experience.
Education
Academic foundation in AI and computing
A combination of postgraduate study in AI and Data Science and an earlier computing degree that built my software and technical foundation.
Loyalist College
Post-Graduate Certificate in Artificial Intelligence & Data Science
Focused on machine learning, data science, applied AI systems, and project-based development. This program helped deepen my work across retrieval systems, forecasting, model evaluation, and deployment-ready AI applications.
London Metropolitan University
BSc (Hons) in Computing
Built my core foundation in computing, programming, software development, and problem solving. This degree helped shape the engineering mindset that now supports my work in machine learning systems and applied AI projects.
Let’s Connect
Open to ML, Applied AI, and GenAI-focused opportunities
I’m currently building projects in machine learning, retrieval systems, and deployable AI products. If you’re hiring for ML or Applied AI roles, I’d be glad to connect.