Miracle Chris-Mba
Machine Learning Engineer
Toronto, ON, Canada
My Journey
Applied Machine Learning Engineer with production experience building RAG, LLM agent workflows, computer vision, recommendation, and backend systems from data pipelines through deployment.
Timeline
Machine Learning Engineer (Co-op)
Ontario Public Service, Toronto, Ontario
Built production RAG and LLM agent systems for security and log intelligence
- Built and deployed an AWS Bedrock RAG pipeline over large-scale historical security and log data, reducing analyst triage time by 35%
- Developed LLM-based agentic workflows for DataLake log streams, improving automated response time by 15%
- Designed evaluation experiments for retrieval relevance, answer usefulness, latency, model performance, and data drift
- Translated evaluation results into Power BI dashboards for security, data, and product stakeholders
- Wrote production Python/ML system code with AWS services, Docker, CI/CD, REST APIs, and monitoring
Diploma - Artificial Intelligence (Co-op)
Fanshawe College, London, Ontario
Completed advanced AI/ML studies with a 4.2 GPA
- Completed coursework in Supervised Learning, Deep Learning, Generative AI, Recommendation Systems, Forecasting & Time Series, ML Deployment, and MLOps
- Built custom CNN models and transfer-learning pipelines using PyTorch, OpenCV, CLIP, ResNet, and custom architectures
- Developed model evaluation and deployment workflows across FastAPI, Railway, and Netlify
Backend Engineer
SHIIP (Remote) - Rhode Island, United States
Built high-throughput data services and REST APIs for real-time logistics workflows
- Built Go and Python services that reduced response times by 40%
- Designed event-driven storage and retrieval systems supporting personalized user experiences
- Integrated third-party data sources with validation and observability, improving workflow reliability by 20%
- Improved API reliability through error handling, asynchronous processing, and database query optimization
Co-Founder & ML / Backend Lead
Copnow (Hybrid) - Ikeja, Nigeria
Co-founded an e-commerce fashion marketplace with QC-backed marketplace workflows
- Built predictive analytics and order-tracking services that improved uptime by 30%
- Developed APIs handling 10,000+ daily requests with 40% lower latency
- Led 3-5 engineers through product discovery, backend architecture, and experimentation
- Used user activity, saves, post clicks, and order events to inform personalization and recommendation features
Fullstack Developer
CURACEL (Remote) - San Francisco, United States
Enhanced application security and user experience
- Enhanced application security features improving user data protection by 60%
- Introduced multi-language feature expanding reach by 15% into non-English markets
Backend Developer
TEEK-TECH (Onsite) - Portharcourt, Nigeria
Built CRUD endpoints for NGO product
- Built and maintained CRUD endpoints enabling 1,000+ users to manage profiles
- Translated web designs into functional front-end code improving load times by 25%
BSc Computer Science
Madonna University, Elele, Rivers
Graduated with thesis on autonomous vehicle development
- Developed sensor-guided autonomous vehicle using Arduino and Ultrasonic Sensors
- Implemented HC-05 Bluetooth for remote controls using Android Application
Core Competencies
Machine Learning
Generative AI & Agents
Backend Engineering
MLOps, Cloud & Data
Leadership
Selected ML Projects
ONA
Object Navigation Assistant for real-time spatial awareness, combining object detection, depth estimation, face detection, and OCR to describe nearby hazards and context.
View projectFriendNet
Custom CNN vision model for friendly-face classification, compared against ResNet and CLIP transfer-learning baselines across accuracy, robustness, latency, and real-world image quality.
View projectOrchestra
Native macOS Rust CLI and daemon orchestrating 7+ AI coding agents across 8+ codebases with deterministic templates, atomic writes, sha256 drift detection, and sub-second Tokio sync.
Certification
AWS Certified Cloud DevOps Engineer
Certified in 2022; experienced deploying ML systems on AWS with Docker and CI/CD pipelines.
Current Focus
Applied ML Systems
Building RAG, LLM agent workflows, computer vision, semantic search, and recommendation systems with measurable evaluation, monitoring, and deployment discipline.
Backend Excellence
Production experience in Python, Go, REST APIs, data-backed logistics systems, model serving, Docker, CI/CD, and cloud deployment.
Leadership & Innovation
Comfortable turning ambiguous operational problems into production ML system designs through stakeholder discovery, product judgment, and iterative delivery.