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Nesto is Canada's leading digital residential mortgage lender and a pioneer in online mortgage lending. The company operates through multiple brands (nesto, CMLS, Nesto Cloud) and has grown to administer over CAD $80 billion in residential and commercial mortgages. Recognized as a Deloitte Fast 50 company for three consecutive years and CLA Lender of the Year, nesto combines over 50 years of mortgage expertise with proprietary cloud and AI technology.
Nesto's AI Stream is building Maestro, an AI underwriting and lending engine that reads documents, applies credit policies automatically, and generates clear, auditable lending decisions in minutes. The team is in active build phase with significant near-term delivery commitments.
As a Senior Applied AI Engineer, you will lead major features and projects end-to-end, surfacing trade-offs clearly to keep delivery on track without compromising quality. You'll design and optimize agentic AI workflows that combine deterministic rules with LLM perception, embedding search, and structured output extraction. You'll build and own evaluation pipelines including golden sets, offline and online evaluations, and regression tracking to measure model quality over time. You'll shape architecture decisions for the AI agent platform, setting high technical standards through code reviews and collaborative problem-solving. You'll use AI tooling and agent harnesses to write and ship code faster, enabling the team to meet deadlines. You'll mentor peers and collaborate with cross-functional teammates to hit delivery timelines.
This is a remote-first role open to candidates anywhere in Canada, with flexibility to work from home or from any of nesto's offices nationwide.
REQUIREMENTS:
- 5+ years of professional software development experience with strong backend and API skills
- Strong Python skills and hands-on experience with LLMs, ideally running them in production (prompting, RAG, or structured extraction)
- Data science, data analysis, or statistics background
- Experience with model implementation, monitoring, and evaluation across the full lifecycle
