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Langgraph Jobs in Quebec (NOW HIRING)

Ironbelly Studios

AI Platform Engineer

Montreal, QC · Hybrid

Design and build the core of GameFrame AI, implementing sophisticated multi-agent (LangGraph Swarm) and single-agent (ReAct) systems that translate user requests into concrete game development ...

Tecsys Inc.

Senior Developer (AI / ML)

Montreal, QC · On-site +1

Experience with LangChain, LangGraph, Strands Agents, or similar frameworks. * Excellent communication and collaboration skills. Nice to Have * AWS Bedrock, AgentCore, Lambda, DynamoDB, API Gateway ...

Valtech

Scientifique des donnees senior

Montreal, QC

LangGraph * Vision par ordinateur * Modeles de generation d'images * Frameworks d'orchestration des LLM * Technologies d'IA open source Si vous ne possedez pas toutes les qualifications mentionnees ...

SAP

SAP iXp Intern - AI Engineer

Montreal, QC

Experience with agentic frameworks (LangChain, LangGraph) or multi-tool orchestration. * Knowledge of data engineering, Kubernetes, and cloud platforms. Where you belong Be part of SAP Next Gen, a ...

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Langgraph information

What is a Langgraph?

Langgraph is a framework designed to build, manage, and orchestrate complex workflows for large language models (LLMs). It allows developers to create directed graphs of language model prompts, tools, and custom logic, making it easier to design multi-step, stateful AI applications. Langgraph is especially useful for building conversational agents, automated workflows, and other applications that require LLMs to interact with data or tools in a structured way.

What are some common challenges faced by Langgraph developers when integrating their workflow with existing AI infrastructure?

Langgraph developers often encounter challenges when integrating their workflow with existing AI infrastructure, such as ensuring compatibility with various large language models and managing data flow across multiple APIs. Coordination with data engineers and machine learning specialists is crucial to align model outputs with business requirements, and adapting to rapidly evolving technologies can require continuous learning. Additionally, optimizing performance and maintaining security standards during integration are key considerations to ensure successful deployment.

What are the key skills and qualifications needed to thrive as a Langgraph engineer, and why are they important?

To thrive as a Langgraph engineer, you need a strong background in software engineering, proficiency in Python, and a solid understanding of AI/ML concepts, usually supported by a degree in computer science or a related field. Familiarity with machine learning frameworks (like TensorFlow or PyTorch), API integrations, and version control systems such as Git is essential. Effective problem-solving, collaboration, and clear communication are crucial soft skills for working with multidisciplinary teams and resolving complex issues. These capabilities are important because they enable the development, scaling, and maintenance of robust AI-driven applications using the Langgraph platform.

What is the difference between Langgraph vs Data Analyst?

AspectLanggraphData Analyst
Required CredentialsTypically requires knowledge of language processing and graph databasesUsually requires a degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI research labs, data-driven organizationsBusiness, finance, healthcare, and marketing sectors
Industry UsageEmerging role in AI and NLP projectsEstablished role in data interpretation and reporting

While Langgraph focuses on language processing and graph database integration, Data Analysts primarily interpret and visualize data to support business decisions. Both roles require analytical skills, but Langgraph specialists often have a background in AI and NLP, whereas Data Analysts typically hold degrees in statistics or related fields.

What job categories do people searching Langgraph jobs in Quebec look for?

The top searched job categories for Langgraph jobs in Quebec are:

Langgraph jobs near you
Infographic showing various Langgraph job openings in Quebec as of August 2026, with employment types broken down into 1% Internship, 91% Full Time, 3% Part Time, and 5% Contract. Highlights an 78% Physical, 6% Hybrid, and 16% Remote job distribution.

QA Lead AI Systems & Models Testing

Jay Analytix

Montreal, QC • On-site

Contractor

Re-posted 12 days ago

Job description

QA Lead AI Systems & Models Testing

Quality Assurance Artificial Intelligence Contract Position

Contract

Montreal, QC

AI / ML Testing

LLM / RAG / LangChain

ABOUT THE ROLE

We are seeking an experienced QA Lead with deep expertise in AI systems testing to join our team on a contract basis in Montreal, Qubec. This role sits at the intersection of quality engineering and artificial intelligence, requiring hands-on proficiency in LLM behavior analysis, RAG pipeline validation, and modern AI orchestration frameworks. You will own the end-to-end test strategy for complex AI products and help define quality standards in a rapidly evolving space.

MUST-HAVE SKILLS

  • Proven QA leadership experience designing and executing test strategies for AI/ML systems or LLM-powered applications.
  • Strong understanding of LLM internals: tokenization, embeddings, attention mechanisms, and inference behavior to anticipate and diagnose failure modes.
  • Hands-on experience with prompt engineering constructing effective prompts, detecting hallucinations, and evaluating outputs across accuracy, tone, coherence, and bias dimensions.
  • Experience testing RAG pipelines and knowledge base integrations, including validation of data quality and retrieval accuracy as they impact model outputs.
  • Familiarity with vector database mechanics: similarity search thresholds, embedding drift, near-duplicate documents, and sparse vs. dense embeddings.
  • Practical experience with LangChain and/or LangGraph able to read chain/graph construction code, identify failure points, and write test harnesses.
  • Ability to validate MCP (Model Context Protocol) integration points, including tool availability and error-handling scenarios.
  • Proficiency applying generative AI evaluation metrics and establishing quality thresholds appropriate for production AI systems.
  • Excellent written and verbal communication in English; bilingualism (English/French) is a plus for the Montreal market.

NICE-TO-HAVE SKILLS

  • Experience with bias detection and safety testing frameworks for AI systems.
  • Exposure to performance and scalability testing of vector databases under high load.
  • Familiarity with CI/CD pipelines for ML model deployment and automated regression testing.
  • Knowledge of responsible AI principles and AI governance frameworks.
  • Contributions to or experience with open-source AI testing or evaluation tooling (e.g., DeepEval, Ragas, PromptFlow).
  • Background in data engineering or data quality practices relevant to AI pipeline inputs.
  • Cloud platform experience (AWS, Azure, or GCP) in the context of deploying or testing AI workloads.

KEY RESPONSIBILITIES

  • Lead design and execution of comprehensive test strategies across AI systems, including prompt evaluation, output quality assessment, and bias/safety analysis.
  • Develop and maintain test harnesses for LangChain and LangGraph-based applications; review chain and graph construction code to proactively surface integration risks.
  • Validate RAG pipeline integrity data ingestion, chunking, retrieval accuracy, and embedding consistency and define edge-case coverage for vector database interactions.
  • Establish and track generative AI quality metrics and thresholds; report on model output quality across multiple evaluation dimensions.
  • Collaborate with ML engineers, data scientists, and product teams to embed quality practices throughout the AI development lifecycle.
  • Document test findings clearly for both technical and non-technical stakeholders.

Contract position based in Montreal, Qubec, Canada On-site / Hybrid