Mastery of advanced prompting techniques (Chain/Tree/Graph of Thought, ReAct, etc.). * Knowledge of both proprietary and open-source model ecosystems (OpenAI, Anthropic, Mistral AI, Hugging Face ...
Mastery of advanced prompting techniques (Chain/Tree/Graph of Thought, ReAct, etc.). * Knowledge of both proprietary and open-source model ecosystems (OpenAI, Anthropic, Mistral AI, Hugging Face ...
A plus: * Able to create and modify materials in unreal (Shader graph, Material Function, Layering, etc.) * Knowledge of Photoshop and Substance painter. Dans ce role, tu devras: * Collaborer ...
A plus: * Able to create and modify materials in unreal (Shader graph, Material Function, Layering, etc.) * Knowledge of Photoshop and Substance painter. Dans ce role, tu devras: * Collaborer ...
Agentic Engineer, Innovation
Montreal, QC · On-site
Develop agentic workflows using LLMs, RAG, APIs, tools, enterprise knowledge sources, memory ... Familiarity with Azure, AKS, PostgreSQL, Key Vault, graph and vector databases, agent orchestration ...
Agentic Engineer, Innovation
Montreal, QC · On-site
Develop agentic workflows using LLMs, RAG, APIs, tools, enterprise knowledge sources, memory ... Familiarity with Azure, AKS, PostgreSQL, Key Vault, graph and vector databases, agent orchestration ...
Lead Animator
Montreal, QC · Remote
Deep knowledge of gameplay and AI animation systems, animation graph/data integration, motion capture pipelines, and rigging/prototyping processes. * Strong practical skill in hand-key facial ...
Lead Animator
Montreal, QC · Remote
Deep knowledge of gameplay and AI animation systems, animation graph/data integration, motion capture pipelines, and rigging/prototyping processes. * Strong practical skill in hand-key facial ...
... loop workflows, knowledge bases, RAG, tool calling, connectors, guardrails, and escalation ... Graph, or Microsoft connectors. * Experience with generative AI platforms and tools beyond ...
... loop workflows, knowledge bases, RAG, tool calling, connectors, guardrails, and escalation ... Graph, or Microsoft connectors. * Experience with generative AI platforms and tools beyond ...
Senior AI Developer
Montreal, QC · On-site
Excellent knowledge of REST API standards, Graph and web services * Experience taking requirements to design and building reusable modules * Experience with debugging, troubleshooting and problem ...
Senior AI Developer
Montreal, QC · On-site
Excellent knowledge of REST API standards, Graph and web services * Experience taking requirements to design and building reusable modules * Experience with debugging, troubleshooting and problem ...
Lead Animator
Montreal, QC · On-site
Deep knowledge of gameplay and AI animation systems, animation graph/data integration, motion capture pipelines, and rigging/prototyping processes. * Strong practical skill in hand-key facial ...
Lead Animator
Montreal, QC · On-site
Deep knowledge of gameplay and AI animation systems, animation graph/data integration, motion capture pipelines, and rigging/prototyping processes. * Strong practical skill in hand-key facial ...
Performance Engineer
Montreal, QC · On-site
... graph inefficiency Diagnose and resolve hitching: garbage collection pauses, streaming stalls ... analysis without a knowledge transfer session Requirements 5+ years of professional game ...
Performance Engineer
Montreal, QC · On-site
... graph inefficiency Diagnose and resolve hitching: garbage collection pauses, streaming stalls ... analysis without a knowledge transfer session Requirements 5+ years of professional game ...
... graph analysis is beneficial. Interest in applying AI-assisted software engineering tools safely ... Knowledge of French and English is required. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan ...
... graph analysis is beneficial. Interest in applying AI-assisted software engineering tools safely ... Knowledge of French and English is required. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan ...
... graph analysis is beneficial. * Interest in applying AI-assisted software engineering tools safely ... Knowledge of French and English is required. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan ...
... graph analysis is beneficial. * Interest in applying AI-assisted software engineering tools safely ... Knowledge of French and English is required. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan ...
Work with modern asynchronous calculation graph platforms and bitempo ral data storage technologies ... Knowledge of French and English is required. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan ...
Work with modern asynchronous calculation graph platforms and bitempo ral data storage technologies ... Knowledge of French and English is required. WHAT YOU CAN EXPECT FROM MORGAN STANLEY: At Morgan ...
2027 Winter Student Opportunities RBC Borealis -Machine Learning Software Engineer, 4 Months- Mon...
Montreal, QC · On-site
Hadoop, Spark) as well as SQL, NoSQL and graph databases is an asset; What's in it for you ... Knowledge, Machine Learning (ML), Software Engineering, Software Product Design Additional Job ...
2027 Winter Student Opportunities RBC Borealis -Machine Learning Software Engineer, 4 Months- Mon...
Montreal, QC · On-site
Hadoop, Spark) as well as SQL, NoSQL and graph databases is an asset; What's in it for you ... Knowledge, Machine Learning (ML), Software Engineering, Software Product Design Additional Job ...
2027 Winter Student Opportunities RBC Borealis -Machine Learning Software Engineer, 4 Months- Mon...
Montreal, QC · On-site
Hadoop, Spark) as well as SQL, NoSQL and graph databases is an asset; What's in it for you ... Knowledge, Machine Learning (ML), Software Engineering, Software Product Design Additional Job ...
2027 Winter Student Opportunities RBC Borealis -Machine Learning Software Engineer, 4 Months- Mon...
Montreal, QC · On-site
Hadoop, Spark) as well as SQL, NoSQL and graph databases is an asset; What's in it for you ... Knowledge, Machine Learning (ML), Software Engineering, Software Product Design Additional Job ...
Knowledge Graph information
See Quebec salary details
$24.5K - $40.5K
9% of jobs
$40.5K - $56.5K
5% of jobs
$56.5K - $72.5K
7% of jobs
$75.9K is the 25th percentile. Wages below this are outliers.
$72.5K - $88.5K
14% of jobs
$88.5K - $104.5K
5% of jobs
The median wage is $115.8K / yr.
$104.5K - $120.5K
13% of jobs
$120.5K - $136.5K
11% of jobs
$136.5K - $152.5K
11% of jobs
$153.2K is the 75th percentile. Wages above this are outliers.
$152.5K - $168.5K
6% of jobs
$168.5K - $184.5K
5% of jobs
$184.5K - $200.5K
14% of jobs
$24.5K
$119.9K
$200.5K
How much do knowledge graph jobs pay per year?
What is a knowledge graph?
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 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 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.
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:
What job categories do people searching Knowledge Graph jobs in Quebec look for?
The top searched job categories for Knowledge Graph jobs in Quebec are:

Full-time
Re-posted yesterday
Key responsibilities
Design, build, train, fine-tune, and deploy sophisticated LLM/GenAI models and techniques.
Support solution architecture by designing scalable, secure, and robust GenAI applications.
Develop and optimize applications powered by GenAI models, including prompt engineering, RAG architectures, and model integration.
Job description
Note: La langue de travail pour ce poste est l'anglais. Une description du poste en français est disponible à la fin de cette offre.
Sia is a next-generation, global management consulting group. Founded in 1999, we were born digital. Today our strategy and management capabilities are augmented by data science, enhanced by creativity and driven by responsibility. We’re optimists for change and we help clients initiate, navigate and benefit from transformation. We believe optimism is a force multiplier, helping clients to mitigate downside and maximize opportunity. With expertise across a broad range of sectors and services, our 3,000 consultants serve clients worldwide from 48 locations in 19 countries. Our expertise delivers results. Our optimism transforms outcomes.
Sia’s AI & Data Business Unit is the powerhouse of our firm’s innovation—merging cutting-edge Data Science, Generative AI, and advanced digital solutions to transform industries. With over 350 experts worldwide, we tackle projects from proof-of-concept to large-scale deployment, always pushing the boundaries of AI capabilities. Our 12 R&D labs in Europe and North America drive continuous research in areas like computer vision, MLOps, and deep learning, partnering closely with our business consultants for real-world impact. By joining Sia’s AI & Data team, you’ll step into a vibrant, collaborative environment that nurtures professional growth and empowers you to shape the future of AI-driven consulting.
Job DescriptionJoin us as an experienced Generative AI Specialist designing and implementing cutting-edge GenAI/LLM solutions across diverse industries. You'll act as a vital bridge between technical teams (Data Science, ML, Platform) to deliver business-centric AI value. You'll guide clients on the optimal path—using techniques like RAG, agents, or fine-tuning—for cost-effective impact.
Your responsibilities extend beyond prompting to architecting robust AI products via benchmarking, prototyping, and validation. You'll orchestrate the full AI workflow, ensuring seamless model integration optimized for performance, security, and scale, while tackling infrastructure challenges. We provide extensive training to support your success. If you're driven to push AI boundaries and help clients rapidly adopt GenAI with confidence, apply to make a real difference.
Responsibilities:
- LLM/GenAI System Development: Design, build, train, fine-tune, and deploy sophisticated AI models leveraging LLMs (e.g., GPT-x, Claude, Gemini, Llama, Mistral) and other generative techniques.
- Assist in Solution Architecture: Support the GenAI Solution Architect in designing robust, scalable, and secure applications.
- Application Development: Develop applications powered by GenAI models (both self-managed and API-accessible) that meet business needs and comply with applicable regulations (GDPR, EU AI Act, model licenses, etc.).
- Advanced Prompt Engineering: Design and optimize effective prompts (e.g., few-shot, Chain/Tree/Graph of Thought, ReAct, Self-reflection, guardrails), balancing simplicity and complexity to enhance analytical capabilities, refine outputs, improve user experience, and control interactions.
- RAG Implementation: Design and implement Retrieval-Augmented Generation (RAG) architectures to improve accuracy and relevance by retrieving information from pre-determined knowledge sources, providing traceability (source attribution).
- Model Selection & Fine-Tuning: Select and fine-tune appropriate models (including multimodal - VLM, SLM - Visual Language Models, Small Language Models) to create higher-quality content (text, image, audio, code, etc.) and maximize business value creation opportunities.
- Integration & Deployment (MLOps): Implement MLOps best practices for the GenAI lifecycle, including automated pipelines (CI/CD), versioning, monitoring, and maintenance in production environments (Cloud platforms like AWS, Azure, GCP). Ensure seamless integration into existing systems and with external tools/APIs, potentially utilizing standardized protocols (MCP).
- Evaluation & Responsible AI: Develop and execute rigorous evaluation frameworks to measure model performance, reliability, fairness, and safety. Ensure adherence to Responsible AI principles and help teams and clients navigate end-to-end security and compliance processes.
- Research & Innovation: Stay abreast of the latest advancements in GenAI techniques, technologies, and frameworks. Experiment with new approaches and contribute to internal knowledge sharing.
- Collaboration: Work effectively within cross-functional teams, communicating complex technical concepts clearly to diverse stakeholders (both technical and non-technical).
- Documentation: Document processes, methodologies, and best practices for knowledge sharing and future reference.
- Use Case Differentiation: Distinguish between use cases suited for Generative AI versus traditional NLP applications (e.g., NER, sentiment analysis).
Education:
- Master's degree or Engineering degree (or equivalent) in Computer Science, Engineering, Data Science, or a related quantitative field.
- Experience: 1-3+ years of hands-on experience in machine learning, software engineering, or data science, with demonstrated experience on complex AI projects specifically involving LLMs and Generative AI.
Required Technical Skills:
- Strong programming skills, particularly in Python.
- Proficiency in at least one major GenAI application framework (LangChain, LlamaIndex, etc.).
- Proven experience in NLP/NLU, vector embeddings, and semantic search.
- Hands-on experience with fine-tuning LLMs.
- Mastery of advanced prompting techniques (Chain/Tree/Graph of Thought, ReAct, etc.).
- Knowledge of both proprietary and open-source model ecosystems (OpenAI, Anthropic, Mistral AI, Hugging Face, models hosted on AWS/Azure/GCP, etc.).
- Experience with Cloud platforms (AWS, Azure, or GCP) and their associated MLOps/AI services.
- Familiarity with MLOps principles, CI/CD tools, Docker, and Git.
Soft Skills:
- Excellent communication skills (written and verbal), ability to explain complex technical concepts simply.
- Strong analytical and complex problem-solving abilities.
- Team player with the ability to collaborate effectively with diverse profiles.
- Curiosity, pioneering spirit, and a demonstrated commitment to continuous learning.
Preferred Qualifications:
- Experience building agentic AI systems (e.g., LangGraph, AutoGen).
- Experience with vector databases (e.g., pgvector, Pinecone, Chroma, OpenSearch).
- Experience developing conversational AI systems / chatbots.
- Familiarity with deep learning frameworks (PyTorch, TensorFlow).
- Understanding of or experience with protocols and standards for connecting LLMs to external tools and databases, such as Model Context Protocol (MCP).
- Experience deploying models at scale in production environments.
Additional Information
Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.
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Description résumée du poste en français:
Nous recherchons un conseiller en IA générative pour rejoindre son équipe AI & Data, au cœur des projets d’innovation du cabinet. Le rôle consiste à concevoir, déployer et mettre en production des solutions GenAI à fort impact pour des clients issus de secteurs variés.
Le ou la spécialiste interviendra sur l’ensemble du cycle de vie des solutions IA :
choix des modèles, prompt engineering avancé, architectures RAG, fine-tuning, intégration et MLOps, tout en assurant performance, sécurité, conformité et IA responsable.
Le poste implique une forte collaboration avec les équipes Data, ML, plateformes et business, ainsi qu’un rôle-conseil auprès des clients pour identifier les meilleurs cas d’usage et accélérer l’adoption de l’IA générative.
Profil recherché : expérience concrète en IA générative et LLM, maîtrise de Python, NLP, embeddings, frameworks GenAI (LangChain, LlamaIndex), cloud (AWS/Azure/GCP) et bonnes pratiques MLOps.
Sia is an equal opportunity employer. All aspects of employment, including hiring, promotion, remuneration, or discipline, are based solely on performance, competence, conduct, or business needs.