Machine Learning Engineer

Zafin is an AI platform company helping regulated institutions modernize how critical work is designed, governed, and delivered. Our technology enables organizations to move faster while maintaining the governance, accountability, and control required in highly regulated environments.

Our portfolio includes Zafin AIOS, an agent orchestration platform for governed AI work; the Zafin Banking Platform, which helps banks modernize product, pricing, offers, billing, loyalty, and relationship management; and Zafin IO, an integration platform that connects data, systems, and workflows across complex enterprise environments.

Headquartered in Toronto, Canada, Zafin partners with leading financial institutions across North America, Europe, the Middle East, Africa, and Asia-Pacific. As AI transforms the future of financial services, we're building the platforms that help regulated organizations adopt AI responsibly and at scale.

What is the Opportunity? 

As a Machine Learning Engineer, part of Zafin's AIOS Product team, you will build the machine learning and AI systems that power the AIOS Knowledge Fabric and help agents reason over enterprise knowledge with accuracy, traceability, and control. 

You will work on practical production systems, including retrieval, ranking, embeddings, model integration, evaluation, data pipelines, model serving, observability, and the workflows that make AI outputs reliable for regulated banking environments. This is a hands-on engineering role focused on turning ML and LLM capabilities into dependable product infrastructure. 

This role is critical for high-quality knowledge, strong evaluation, and reliable model behavior. You will help build the foundation that allows customers to use AI agents with confidence, accountability, and measurable business value. 

What Will You Do? 

• Design, build, test, and maintain ML and AI capabilities for AIOS, with a focus on knowledge retrieval, model integration, and production reliability.
• Develop systems for embeddings, vector search, ranking, retrieval-augmented generation, knowledge ingestion, and knowledge quality improvement.
• Build and improve model evaluation workflows, test sets, scoring approaches, quality metrics, and monitoring for AI and LLM-based systems.
• Partner with AI engineers, data engineers, product managers, architects, and domain experts to translate product needs into ML system designs and working software.
• Write production-quality Python or equivalent code, including services, pipelines, automation, tests, and integration points with product systems.