MLOps Engineer (Part-time Remote)

Company Description

Macrotrend Analytics is a Canadian analytics firm that specializes in Business Intelligence, Machine Learning, and advanced economic analysis. We design AI-driven solutions such as dynamic pricing, demand forecasting, personalized marketing, and MLOps deployment to empower e-commerce and retail businesses to optimize revenue and efficiency. Our expertise spans the entire analytics lifecycle, including AI readiness, model development, CI/CD pipeline deployment, and optimization. Our offerings range from ready-to-install applications for Shopify/WooCommerce to fully customized machine learning and economic forecasting tools.

Learn More About Us

🌐 https://mtanalytics.ca/

Role Description

This is a part-time remote role for an MLOps Engineer who will play a key role in deploying and managing Machine Learning models and pipelines.

Responsibilities:

MLOps (primary)

• Build, deploy, and scale ML services on AWS / GCP / Azure with GPU support.

• Develop robust FastAPI + Docker microservices for real-time inference.

• Implement CI/CD pipelines.

• Configure model monitoring, logging, drift detection, and automated retraining.

• Manage environment reproducibility, versioning, and containerized workflows.

• Integrate ML services with external systems (Shopify, WooCommerce, backends).

• Optimize inference for high-performance, low-latency environments.

Machine Learning (secondary but required)

• Train and optimize ML models for clients (classification, regression, NLP, forecasting).

• Work with transformer models (MiniLM/BERT) and GPU training pipelines.

• Convert prototypes into clean, production-grade ML code.

• Build batch and streaming prediction pipelines for business applications.

Qualifications

MLOps:

• Strong experience deploying models to cloud platforms (AWS/GCP/Azure).
• Strong proficiency with FastAPI, Docker, CI/CD pipelines.
• Experience with production monitoring tools.
• Strong understanding of scaling, API reliability, and secure ML deployment.

Machine Learning:

· Demonstrated experience developing and deploying ML models for real business use cases.

· Advanced proficiency in Python and core ML frameworks (scikit-learn, XGBoost, Hugging Face Transformers, PyTorch).

· Experience with applied NLP, customer behavior analytics, or time-series forecasting is considered an asset.

Mandatory Requirement (Important)

We only consider candidates with real-world production experience, meaning:

• You have deployed ML systems used by actual clients or companies.
• You have built real APIs, dashboards, or automated pipelines.
• You can demonstrate your work.

→ Application MUST include A link to a video demonstration of at least one real-world ML/MLOps project you completed.

What We Offer

• Competitive compensation

• Flexible, fully remote work environment with autonomy and trust

• Opportunity to build production-grade ML systems used by real businesses

• Long-term collaboration and growth potential within an expanding Canadian startup