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.
Robust 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.
Skilled French required, technical English appreciated.
Based in or around Montreal, authorized to work in Canada.

Valuable 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.
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📌 Data / Ai Engineer — Building Decarbonization Saas — Montreal
🏢 Abbeal
📍 Montreal