AI Platform Architect

At Intact Insurance, we're building next-generation AI capabilities that leverage Generative AI, Machine Learning, and cloud-native technologies. As an AI Platform Architect, you will be responsible for designing and governing the enterprise AI platform, enabling the secure deployment of AI solutions across the business.

You will define technical standards, platform architecture, and engineering patterns that support scalable AI development, model deployment, and intelligent automation across a modern Azure-based ecosystem.

Responsibilities - Design and implement enterprise AI platform architecture leveraging Azure AI Foundry, Azure OpenAI, Azure Machine Learning, and Azure Kubernetes Service (AKS). - Define architecture patterns for Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, and Prompt Engineering frameworks. - Develop platform standards for MLOps and LLMOps including model deployment, monitoring, versioning, and lifecycle management. - Architect data solutions using Azure Data Lake, Databricks, Synapse Analytics, and Microsoft Fabric. - Design vector search and knowledge retrieval solutions using vector databases, embeddings, semantic search, and Retrieval-Augmented Generation architectures. - Establish Infrastructure as Code (IaC) standards using Terraform, Bicep, ARM Templates, and GitHub Actions. - Architect containerised solutions using Docker, Kubernetes, microservices, and event-driven architectures. - Define API integration patterns using REST APIs, GraphQL, service buses, and cloud-native messaging services. Experience - Proven experience designing enterprise AI, Machine Learning, or Advanced Analytics platforms within cloud environments. - Strong expertise with Microsoft Azure including Azure OpenAI, Azure AI Foundry, Azure Machine Learning, Azure Data Factory, Azure Data Lake, AKS, and Microsoft Fabric. - Experience developing Generative AI solutions using LLMs, RAG, AI Agents, prompt engineering, embeddings, vector databases, and semantic search technologies. - Hands-on knowledge of MLOps and LLMOps practices including CI/CD pipelines, model governance, feature stores, model monitoring, and experimentation frameworks. - Strong development experience with Python, SQL, PySpark, Spark, and machine learning libraries such as TensorFlow, PyTorch, Scikit-learn, or LangChain. - Experience working with Databricks, Synapse Analytics, distributed computing, and large-scale data processing platforms. - Knowledge of containerisation technologies including Docker, Kubernetes, Helm, and cloud-native application architectures. - Experience implementing Infrastructure as Code using Terraform, Bicep, ARM Templates, Azure DevOps, and GitHub. - Strong understanding of API architecture, event-driven systems, messaging platforms, and enterprise integration patterns.