I'm a Machine Learning & AI Engineer and MLOps Specialist based in North Vancouver, Canada, with over seven years of experience designing, deploying and maintaining scalable AI solutions. I specialize in generative AI, large language models and retrieval-augmented generation applications, as well as MLOps, data engineering and cloud-native deployments.
Generative AI & NLP: Build and fine-tune large language models and transformer-based systems; design prompt engineering and retrieval-augmented generation (RAG) pipelines using LangChain and vector databases; create summarization and Q&A systems.
Machine Learning Engineering: Develop end-to-end ML pipelines from data ingestion and feature engineering to model training and evaluation; work with traditional ML and deep learning frameworks including scikit-learn, TensorFlow, PyTorch, CatBoost and DistilBERT.
MLOps & LLMOps: Implement CI/CD workflows, experiment tracking, model registry and data versioning using MLflow, DVC, Airflow, Vertex AI, Azure DevOps and GitHub Actions; deploy models across cloud platforms (Azure ML, AWS SageMaker, Google Vertex AI) and monitor them using Grafana, Prometheus and Cloud Logging.
Data Engineering: Design and maintain robust ETL/ELT pipelines on BigQuery, BigQuery, Dataflow and cloud functions; build and manage data warehouses, lakes and lakehouses; ensure data quality, scalability and reliability.
Project Leadership & Collaboration: Collaborate with cross-functional teams to align AI solutions with business goals; lead projects from conception to deployment; mentor team members and promote best practices in Agile development.
| Category | Tools & Technologies |
|---|---|
| Programming & Data | Python, SQL, Bash, LookML, JavaScript |
| ML & Deep Learning | scikit-learn, XGBoost, TensorFlow, PyTorch, CatBoost, OR-Tools, DistilBERT, Hugging Face, Keras, LangChain |
| Generative AI & NLP | Large Language Models, transformer fine-tuning, prompt engineering, retrieval-augmented generation (RAG) |
| MLOps & Data Engineering | MLflow, DVC, Airflow, Vertex AI, Azure ML, AWS SageMaker, Docker, Kubernetes, Bitbucket Pipelines, Azure DevOps, Git |
| Data Warehousing | ETL/ELT, BigQuery, Redash, MySQL, PostgreSQL, Dataflow |
| Monitoring & Logging | Grafana, Prometheus, Azure Monitor, Cloud Logging |
| Collaboration Tools | Jira, Confluence, Slack, Looker, Tableau, Matplotlib, Plotly, Notion |
- TransCostML – Command-line tool for transport price estimation; extracts delivery data, preprocesses it and trains ensemble models (Random Forest, XGBoost, stacking), achieving ~18% improvement in MAE. Repository
- Gomat Markup Optimization – Conversion-probability and markup-optimization models using CatBoost and Random Forest; integrated FastAPI inference service with MLflow tracking and CI/CD pipelines; deployed on Azure ML Studio. Repository
- GoSource Routing Optimization – Proof-of-concept route-optimization system using OR-Tools and Flask to generate optimal vehicle routes with CLI tools and Slack notifications. Repository
- Ads Recommendation System – Real-time ads recommendation engine on GCP Vertex AI and BigQuery, providing personalized ads that improved click-through rates by 35%. Repository
- Participedia Capstone – Multi-task learning pipeline using DistilBERT, deployed with Kubernetes and Vertex AI, generating embeddings and classifications for participatory democracy data. Repository
- Loan Approval Prediction – Classification models (Logistic Regression, SVM, Random Forest, Gradient Boosting) to predict loan approvals, achieving an F1-score of 0.947. Repository
You can find my detailed resume here.
- Email: dennisdarko0909@gmail.com
- LinkedIn: Dennis Darko
- GitHub: dendarko