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.
We recognize that skills and competencies can be developed through a variety of experiences, both professional and personal. Even if you don't meet every single requirement listed, we still encourage you to apply. Your unique background and perspective could be exactly what we're looking for.
This job posting represents an active opportunity within our team. All applications are reviewed directly by our recruitment professionals, and no AI is used in the screening or selection process.
Let us learn about you! Apply today.
You must submit an online application to be considered for any position with us. This position will be posted until filled.
At Schneider, we believe that every employee is a talent who deserves equal opportunities. This means you matter. Every individual needs to feel valued, supported, and treated fairly to do their best work.
Our Total Rewards is our way of saying: "We see you. We value you". It's more than just pay and benefits- it's a meaningful investment in you. It is designed for you to perform, grow, feel safe, and elevate your potential to shine as an impact maker.
The expected pay range is CAD 114,750 - CAD 162,000 per year. This pay range includes base pay and short-term incentives.
For Ontario applicants, this posting complies with provincial law which does not require disclosure of the pay range. Inquiries regarding the pay range can be directed to the recruiting / hiring manager.
Schneider Electric is there when it matters most to you
Our Total Rewards package outlines all the benefits and support you'll enjoy as part of the Schneider Electric team:
Care for yourself and your family. We ensure you feel secure with benefits that help you and your family thrive: insurance coverage for health, inclusive of medical, dental, vision, basic life, accident, and disability insurance; telemedicine program; paid family leaves; parental and care leave; employee assistant program with mental, family, and legal counselling and flexible work arrangements.
Invest and plan your future. We help you plan and invest for the future with competitive pay and programs: base salary; incentive plans; employee share ownership plan; retirement plan with employer pension match.
Grow your skills and career. We commit to helping you grow with ongoing performance and development conversations, Senior Talent Program, global career opportunities, access to our Schneider Career Hub for new positions, projects, and mentors, and learning platforms like Coursera to equip you with the skills for today and tomorrow.
Team up in the workplace. We encourage teaming up through project opportunities on the Schneider Career Hub, smart ways to collaborate, celebrating contributions via a recognition program (including service anniversary), sharing your voice in our engagement survey, and fostering an inclusive, caring workplace.
Support your community. We make a difference in your community with volunteer leave, programs through the Schneider Electric Foundation , and initiatives that support youth education.
Looking to make an IMPACT with your career? When you are thinking about joining a new team, culture matters. At Schneider Electric, our values and behaviors are the foundation for creating a great culture to support business success. We believe that our IMPACT values - Inclusion, Mastery, Purpose, Action, Curiosity, Teamwork - starts with us.
IMPACT is also your invitation to join Schneider Electric where you can contribute to turning sustainability ambition into actions, no matter what role you play. It is a call to connect your career with the ambition of achieving a more resilient, efficient, and sustainable world.
We are looking for IMPACT Makers; exceptional people who turn sustainability ambitions into actions at the intersection of automation, electrification, and digitization. We celebrate IMPACT Makers and believe everyone has the potential to be one.
Become an IMPACT Maker with Schneider Electric - apply today!
€40 billion global revenue
+9% organic growth
150 000+ employees in 100+ countries
You must submit an online application to be considered for any position with us. This position will be posted until filled.
Schneider Electric aspires to be the most inclusive and caring company in the world, by providing equitable opportunities to everyone, everywhere, and ensuring all employees feel uniquely valued and safe to contribute their best. We mirror the diversity of the communities in which we operate, and 'inclusion' is one of our core values. We believe our differences make us stronger as a company and as individuals and we are committed to championing inclusivity in everything we do.
At Schneider Electric, we uphold the highest standards of ethics and compliance, and we believe that trust is a foundational value. Our Trust Charter is our Code of Conduct and demonstrates our commitment to ethics, safety, sustainability, quality and cybersecurity, underpinning every aspect of our business and our willingness to behave and respond respectfully and in good faith to all our stakeholders. You can find out more about our Trust Charter here
Schneider Electric is an Equal Opportunity Employer. It is our policy to provide equal employment and advancement opportunities in the areas of recruiting, hiring, training, transferring, and promoting all qualified individuals regardless of race, religion, color, gender, disability, national origin, ancestry, age, military status, sexual orientation, marital status, or any other legally protected characteristic or conduct.
Responsibilities
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.
