AI Solution Architect

Responsibilities: -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.).