AI Solution Architect

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.).