Applied AI & Data Engineer — Energy Data Pipelines

Abbeal SAS; Montreal; On-site; CAD 90,000 - 130,000; Full time

Job summary

Abbeal SAS in Montreal builds climate software that helps real estate portfolio managers, utilities and engineers decarbonize buildings. This role focuses on reconciling energy data, building scalable pipelines and deploying applied AI in production.

You will work with Python, SQL and a cloud data warehouse, shipping document extraction, data enrichment and decision-support components, with emphasis on production-readiness and observability.

Qualifications

• 5+ years in data engineering, ML engineering or applied AI.
• Strong Python and SQL, cloud data warehouse experience.
• Real production experience, not prototyping only.
• French required, English appreciated.
• Based in or around Montreal, authorized to work in Canada.

Responsibilities

• 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.

Skills

Python

SQL

Cloud Data Warehouse

MLOps

Job description

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

• Python
• SQL
• Cloud Data Warehouse
• MLOps

Apply ↓ Based on experience and engagement model — to be discussed

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.

Apply