Data / AI Engineer — Building Decarbonization SaaS — Montreal

Reconcile heterogeneous energy data sources and put applied AI into production at a Quebec-based climate software company.

• Python
• SQL
• Cloud Data Warehouse
• MLOps

Our client is a Montreal-based software company. Its SaaS platform helps real estate portfolio managers, utilities and engineering consultancies decide and act on building decarbonization.

Context

Energy data is the backbone of the product: consumption readings, building characteristics, tariffs, regulatory frameworks. Many sources, few shared formats, and a simulation engine that needs clean inputs to cross-check hundreds of scenarios per building.

Headcount grew by roughly 83% over the past year, and the company is opening a data and applied AI workstream as the platform scales.

The role

• Build and harden the pipelines that ingest and reconcile energy data sources.
• Model data so it serves both the product and analytics.
• Ship applied AI components to production: document extraction, enrichment, decision support.
• Lay the foundations: data quality, observability, cost control, reproducibility.

What we are looking for

• 5+ years in data engineering, ML engineering or applied AI.
• Strong Python and SQL, comfortable with a cloud data warehouse.
• Real production experience, not prototyping only.
• Exposure to LLMs or agentic systems in production is a genuine plus.
• Professional French required, technical English appreciated.
• Based in or around Montreal, authorized to work in Canada.

Good to know

• The exact scope (data platform, ML, generative AI) is clarified during the first conversation with Abbeal.

Details

• Based in Montreal, Quebec.
• Two possible formats: direct hire with the client, or contracting through Abbeal.
• Compensation based on experience and engagement model.
• Short process: one qualification call with Abbeal, then a meeting with the client's technical team.