Senior AI Data Engineering Specialist

Join a new Montréal-based team and help transform product information, semantic models, documentation, scripts, and operational data into reliable foundations for AI-enabled applications.

As part of the Energy Intelligence initiative, you will contribute to capabilities that support multiple products, engineering workflows, and lines of business.

You will bring together subject-matter expertise, semantic modeling, and applied AI engineering to make complex information easier for models to retrieve, understand, and use.

This includes building reliable pipelines and processes to collect, structure, contextualize, govern, and evaluate content from multiple sources, with the appropriate levels of quality, traceability, and access control.

You have experience preparing data or structured knowledge for AI-enabled applications and enjoy bringing clarity to complex information through collaboration.

You do not need to know every aspect of our industry from the start.

We value sound data foundations, relevant hands-on experience, and the ability to learn alongside subject-matter and semantic modeling specialists.

What you'll be doing


* Discover, collect, classify, normalize, and curate data for AI use cases.


* Build data pipelines for Building Management System (BMS) scripts, documentation, metadata, semantic models, and other engineering assets.


* Establish data lineage and provenance, along with quality, versioning, and access controls.


* Prepare datasets for training, validation, benchmarking, and regression testing.


* Connect ontologies and knowledge graphs with AI retrieval and grounding systems.


* Define strategies for chunking, indexing, embeddings, retrieval, and metadata.


* Design processes for expert review, labeling, annotation, and feedback.


* Identify duplicate content, inconsistent tagging, sensitive information, and quality issues.


* Partner with legal, cybersecurity, privacy, and product teams to define appropriate data use.


* Assess data coverage and identify gaps that materially affect model performance.

What you bring


* A background in data engineering, knowledge engineering, or data pipelines for machine learning.


* Solid skills in data modeling and metadata management.


* Proficiency in building reliable data pipelines using Python, SQL, or comparable technologies.


* Hands-on experience preparing data for machine-learning or LLM applications.


* An understanding of embeddings, vector search, RAG, and datasets used for evaluation.


* The ability to collaborate effectively with domain experts and ontology specialists.

You might also have


* Familiarity with RDF, OWL, knowledge graphs, or semantic databases.


* A background in processing source code or domain-specific languages.


* Knowledge of technical documentation and engineering data.


* Experience in applied AI, building technologies, energy management, automation, or IoT systems.
...