Posted last month
AI Solution Architect
@ CGI
Montréal, Quebec
SIGNALS
Overview
CGI is hiring an AI Solution Architect to design end-to-end AI and machine learning solutions aligned with clients' business objectives. The role covers LLM, RAG, AI agent and MLOps architectures, cloud technology choices, and mentoring internal teams and clients. The posting requires 7+ years of IT architecture experience, 3+ of them in AI/ML, and bilingual French and English.
Responsibilities:
- Concevoir des architectures IA et AA de bout en bout
- Choisir les technologies cloud et ML et justifier les compromis
- Intégrer les solutions IA aux systèmes existants avec les équipes
- Accompagner l'adoption des pratiques MLOps
- Réaliser des preuves de concept et prototypes
Requirements summary:
- 7 ans minimum en architecture de solutions TI, dont 3 ans en IA/AA
- Expertise des plateformes cloud Azure, AWS ou GCP
- Connaissance de LLM, RAG, agents IA et MLOps
- Connaissance de la gouvernance des données, de la sécurité IA et de l'éthique algorithmique
- Bilinguisme français et anglais, excellentes habiletés de communication
Tools & skills
Source and dates
Source type not verified · njoyn
- Employer posting date
- 2026-09-23
- First seen by our source
- 2026-10-17
- Source record updated
- 2026-10-17
These dates describe the source record, not a live verification that the employer is still hiring. Check the original posting.
Job details
| Seniority | Senior |
| Department | Software development |
| Experience | 7+ years |
| Hours per week | 40 (estimated) |
| Work setting | Office, Client sites |
| Position | Mostly sitting |
| Physical demands | physical low · computer high |
| Languages | English, French |
Summary and details written by AI from the posting; the employer’s posting is the authority.
CGI
| Field | Value |
|---|---|
| Company size | 10001+ |
| Headquarters | E-Commerce Place |
| Open jobs on Jobsy | 514 |
Job description
Design and architect end to end artificial intelligence (AI) and machine learning (ML) solutions, aligned with clients' business needs and strategic objectives.
Assess client needs and propose tailored AI architectures, including large language models (LLM), RAG (Retrieval Augmented Generation), AI agents, and MLOps pipelines.
Define technology choices (cloud platforms, ML frameworks, generative AI tools) and justify trade offs to stakeholders.
Collaborate with data, software development, and security teams to ensure the coherent integration of AI solutions into existing systems.
Design scalable, secure, and responsible architectures (ethical AI, bias, data governance, compliance).
Support teams in adopting MLOps practices: automation of training pipelines, deployment, monitoring, and model retraining.
Advise clients on cost optimization for AI model inference and training in cloud environments.
Conduct proofs of concept (PoC) and prototypes to validate the technical feasibility of proposed solutions.
Carry out technology watch in AI/ML and integrate the latest advancements (generative AI, LLM, multimodal) into architecture recommendations.
Act as a technical reference and mentor for internal teams and clients in the adoption of AI technologies.
Required Qualifications:
Minimum 7 years of experience in IT solution architecture, including at least 3 years focused on AI/ML.
Expertise in designing AI/ML solutions on cloud platforms (Azure, AWS, or GCP), including managed services (Azure OpenAI, SageMaker, Vertex AI, etc.).
Solid knowledge of ML/AI frameworks: TensorFlow, PyTorch, Scikit learn, LangChain, LlamaIndex.
Experience with LLMs (GPT 4, Claude, Llama) and RAG type architectures, AI agents, and fine tuning.
Proficiency in MLOps practices (MLflow, Kubeflow, Azure ML Pipelines, etc.) and CI/CD pipelines for ML models.
Knowledge of data governance principles, AI security, and algorithmic ethics.
Excellent communication skills to explain technical concepts to non technical stakeholders.
Bilingual (French and English), with advanced writing and presentation skills.
Additional Assets:
AI/ML certification on a cloud platform (e.g.: Azure AI Engineer, AWS ML Specialty, GCP Professional ML Engineer).
Experience with modern data architectures (lakehouses, feature stores, data mesh).
Knowledge of responsible AI tools (Responsible AI, Fairlearn, AI Explainability 360).
Experience deploying multimodal solutions (text, image, audio, video).
Familiarity with AI related regulatory standards (Law 25, EU AI Act, etc.).
Assess client needs and propose tailored AI architectures, including large language models (LLM), RAG (Retrieval Augmented Generation), AI agents, and MLOps pipelines.
Define technology choices (cloud platforms, ML frameworks, generative AI tools) and justify trade offs to stakeholders.
Collaborate with data, software development, and security teams to ensure the coherent integration of AI solutions into existing systems.
Design scalable, secure, and responsible architectures (ethical AI, bias, data governance, compliance).
Support teams in adopting MLOps practices: automation of training pipelines, deployment, monitoring, and model retraining.
Advise clients on cost optimization for AI model inference and training in cloud environments.
Conduct proofs of concept (PoC) and prototypes to validate the technical feasibility of proposed solutions.
Carry out technology watch in AI/ML and integrate the latest advancements (generative AI, LLM, multimodal) into architecture recommendations.
Act as a technical reference and mentor for internal teams and clients in the adoption of AI technologies.
Required Qualifications:
Minimum 7 years of experience in IT solution architecture, including at least 3 years focused on AI/ML.
Expertise in designing AI/ML solutions on cloud platforms (Azure, AWS, or GCP), including managed services (Azure OpenAI, SageMaker, Vertex AI, etc.).
Solid knowledge of ML/AI frameworks: TensorFlow, PyTorch, Scikit learn, LangChain, LlamaIndex.
Experience with LLMs (GPT 4, Claude, Llama) and RAG type architectures, AI agents, and fine tuning.
Proficiency in MLOps practices (MLflow, Kubeflow, Azure ML Pipelines, etc.) and CI/CD pipelines for ML models.
Knowledge of data governance principles, AI security, and algorithmic ethics.
Excellent communication skills to explain technical concepts to non technical stakeholders.
Bilingual (French and English), with advanced writing and presentation skills.
Additional Assets:
AI/ML certification on a cloud platform (e.g.: Azure AI Engineer, AWS ML Specialty, GCP Professional ML Engineer).
Experience with modern data architectures (lakehouses, feature stores, data mesh).
Knowledge of responsible AI tools (Responsible AI, Fairlearn, AI Explainability 360).
Experience deploying multimodal solutions (text, image, audio, video).
Familiarity with AI related regulatory standards (Law 25, EU AI Act, etc.).
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