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Knowledge Graph Jobs in Quebec (NOW HIRING)

SAP

Senior AI Machine Learning Engineer

Montreal, QC · On-site

Geometric ML Knowledge · Develop and optimize: o Graph Neural Networks (GNNs), Graph Transformers, and Relational Transformers o Self-supervised, contrastive, and related pretraining strategies for ...

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Knowledge Graph information

See Quebec salary details

$24.5K

$119.9K

$200.5K

How much do knowledge graph jobs pay per year?

As of Jul 6, 2026, the average yearly pay for knowledge graph in Quebec is $119,862.00, according to ZipRecruiter salary data. Most workers in this role earn between $76,500.00 and $165,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive in the Knowledge Graph position, and why are they important?

To thrive as a Knowledge Graph Engineer, you need strong skills in semantic web technologies, ontology modeling, and data integration, typically supported by a background in computer science or data science. Familiarity with tools like RDF, SPARQL, OWL, and knowledge graph platforms (e.g., Neo4j, GraphDB) is common, and certifications in data engineering or semantic technologies are beneficial. Effective communication, problem-solving abilities, and cross-functional collaboration are valuable soft skills in this field. These competencies are crucial for designing, implementing, and maintaining knowledge graphs that enable advanced data discovery and insights for organizations.

Is ML a high paying job?

Machine Learning (ML) roles, including positions like ML engineer or data scientist, are generally well-paid due to the specialized skills required, such as programming, statistics, and knowledge of algorithms. Salaries tend to be higher than average in tech hubs and often increase with experience, certifications, and proficiency in tools like Python, TensorFlow, or PyTorch.

What is a knowledge graph job description?

A knowledge graph job description typically involves designing, developing, and maintaining knowledge graphs that organize and connect data for improved search, reasoning, and data integration. The role often requires skills in data modeling, graph databases like Neo4j, and understanding of semantic technologies such as RDF and OWL. Professionals in this field may work with data scientists, software engineers, and domain experts to ensure accurate and efficient knowledge representation.

What is a Knowledge Graph job?

A Knowledge Graph job typically involves designing, building, and maintaining structured representations of data that map relationships between entities. Professionals in this role work with technologies like RDF, SPARQL, ontologies, and graph databases to enhance data integration, retrieval, and reasoning. These jobs are common in AI, search, and data science fields, helping organizations improve knowledge discovery and decision-making.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-level position in artificial intelligence, such as AI research director, senior machine learning engineer, or AI product executive, often requiring advanced skills in data science, programming, and deep learning. These roles usually involve leadership, strategic planning, and expertise in tools like TensorFlow or PyTorch, with compensation reflecting experience and impact. Such salaries are rare and generally found in top tech companies or specialized AI firms.

What engineer makes $500,000 a year?

Senior data engineers or machine learning engineers working in high-demand industries such as technology, finance, or AI can earn salaries around $500,000 annually, especially with extensive experience, advanced skills in big data tools, and relevant certifications. Compensation varies based on location, company size, and individual expertise.

What are some typical daily responsibilities of a Knowledge Graph Engineer?

As a Knowledge Graph Engineer, your typical day involves designing and developing ontologies, integrating diverse data sources, and implementing graph-based data models to enhance information accessibility. You may work closely with data scientists, software developers, and business analysts to gather requirements and translate them into scalable knowledge graph solutions. Regular tasks include writing SPARQL queries, performing data mapping, maintaining documentation, and troubleshooting graph data issues. Collaboration and ongoing learning are integral as this field rapidly evolves with new tools and best practices.

What are popular job titles related to Knowledge Graph jobs in Quebec? For Knowledge Graph jobs in Quebec, the most frequently searched job titles are:
Knowledge Graph jobs near you
Product Manager - Operational Technology

Product Manager - Operational Technology

AspenTech

Montreal, QC

Full-time

Posted 23 days ago

AspenTech rating

8.4

Company rating: 8.4 out of 10

Based on 7 frontline employees who took The Breakroom Quiz

71st of 202 rated software companies

Job description

The driving force behind our success has always been the people of AspenTech. What drives us, is our aspiration, our desire and ambition to keep pushing the envelope, overcoming any hurdle, challenging the status quo to continually find a better way. You will experience these qualities of passion, pride and aspiration in many ways - from a rich set of career development programs to support of community service projects to social events that foster fun and relationship building across our global community.

The RoleWe are seeking results-driven product manager to help define and drive the product strategy for our Enterprise Operations Platform (EOP). This role demands a strategic thinker with deep product management experience, technical fluency in industrial automation, and a passion for transforming operational data into actionable intelligence. The ideal candidate will champion the use of AI, semantic modeling, and data contextualization to unlock the full value of OT data across diverse automation systems.Your Impact
  • Strategic Vision:
  • Develop and execute a product strategy that integrates AI-driven data contextualization and semantic modeling into the EOP roadmap.
  • Champion the foundational Data Fabric, enabling scalable ingestion, harmonization, and semantic enrichment of OT data.
  • Drive adoption of knowledge graphs and ontologies to unify data across disparate automation systems and domains.
  • Evangelize the strategic role of contextualized data in enabling predictive analytics, autonomous workflows, and digital twins.
  • Represent the platform's vision in internal and external forums, positioning the company as a thought leader in intelligent operations.
  • Product Management:
  • Lead innovation initiatives focused on transforming raw industrial data into contextualized, actionable insights using AI and semantic technologies.
  • Monitor market trends in AI, data fabrics, and industrial knowledge graphs to ensure competitive differentiation.
  • Guide the development of intelligent data services (e.g., semantic search, anomaly detection, root cause analysis) powered by machine learning.
  • Develop product roadmaps that incorporate semantic interoperability, data lineage, and explainability.
  • Stakeholder Management:
  • Foster collaboration between product management and R&D to deliver intelligent, scalable solutions.
  • Engage with customers and partners to co-develop ontologies and contextual models tailored to industry-specific needs.
  • Qualifications & Experience:
  • Proven leadership in product management with a focus on data platforms, AI, or industrial software.
  • Deep understanding of OT-data domains, industrial automation systems, and data contextualization challenges.
  • Experience with semantic technologies and knowledge graph architectures.
  • Familiarity with AI/ML techniques for time-series analysis, anomaly detection, and predictive modeling.
  • Core Competencies:
  • Customer Centricity: Designs products that deliver contextualized insights and real-world value.
  • Visionary Thinking: Inspires teams around a future of intelligent, autonomous operations.
  • Innovation Leadership: Prioritizes semantic modeling and AI as core differentiators.
  • Results Orientation: Aligns data intelligence initiatives with business outcomes.
  • Strategic Acumen: Navigates complex data ecosystems with clarity and foresight.
  • Decisive Execution: Makes informed decisions grounded in data and domain expertise.
What You'll Need
  • 5+ years of demonstrated skills in product management.
  • Experience in the Process Industries will be a strong advantage.
  • BS in Chemical/Mechanical Engineering/Electrical Engineering required. MBA/Advanced degree (or global equivalent) in a related field preferred.
  • Language requirement: French: A1 (advanced) and English level C1 (advanced), the position requires interactions with international client.
  • Experience of cloud-based as well as on-prem technologies.
  • Experience with AI also an advantage.
  • Strong individual initiative and ownership, driven to achieve results, and motivated by impact. Extremely well organized, detail-oriented, metrics driven, and possessing a high level of integrity.
  • Demonstrated business acumen and industry insights to assess the tradeoffs across technology, operations, process, people, cost, and industry for solutions.
  • Strong sense of teamwork and collaboration, particularly cross-organizationally and cross-functionally in a matrix organization.
  • Business travel of approximately 20 percent yearly is expected for this position.
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