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.

Positive 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.

📌 Data / AI Engineer — Building Decarbonization SaaS — Montreal
🏢 Abbeal
📍 Montreal