AI Architect / Toronto, ON (Hybrid) - FTE at AceStack — NeverHard

We are seeking an experienced AI Architect to lead the design, development, and implementation of enterprise-scale AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions. The ideal candidate will translate business requirements into scalable, secure, governed, and production-ready AI architectures while providing technical leadership across engineering, data science, and business teams. Key Responsibilities Own end-to-end AI solution architecture, from use-case discovery and feasibility assessment to experimentation, production deployment, and operational monitoring. Translate business objectives into AI architecture designs, defining use cases, architecture patterns, decision boundaries, acceptance criteria, and measurable business outcomes. Architect enterprise AI solutions integrating foundation models, RAG pipelines, agent orchestration, vector databases, enterprise data sources, APIs, cloud infrastructure, and human-in-the-loop workflows. Design scalable, secure, cloud-native AI platforms with appropriate identity and access management, integration patterns, and infrastructure controls. Establish AI evaluation and governance frameworks covering model quality, accuracy, groundedness, hallucinations, security, privacy, bias, fairness, compliance, performance, and cost optimization. Define MLOps/LLMOps practices, including deployment pipelines, model and prompt versioning, observability, monitoring, drift detection, rollback strategies, and production readiness. Implement responsible AI, AI safety, model risk management, privacy engineering, and security controls aligned with enterprise policies and regulatory requirements. Provide architecture leadership through technical reviews, design assurance, solution validation, and guidance to engineering and data science teams. Communicate architectural decisions, technical trade-offs, risks, and business value to senior business and technology stakeholders. Required Technical Skills Proven experience designing and delivering end-to-end AI, Generative AI, Machine Learning, RAG, and Agentic AI solutions. Strong understanding of enterprise architecture, solution design, scalability, and production deployment. Generative AI, RAG & Agentic AI Hands-on expertise in foundation models, Large Language Models (LLMs), prompt engineering, embeddings, vector databases, and semantic search. Experience building RAG pipelines, tool/function calling, agent orchestration, and AI guardrails. Experience with Azure AI Services, Azure OpenAI, model hosting, REST APIs, and enterprise integration architecture. Knowledge of event-driven architectures, containers, cloud-native infrastructure, and identity and access management. MLOps/LLMOps & Production Operations Experience implementing AI/ML deployment pipelines, model evaluation, observability, monitoring, and version control. Understanding of model drift detection, rollback mechanisms, performance optimization, and production support. Responsible AI, Security & Governance Knowledge of AI governance, model risk management, privacy, security controls, and regulatory compliance. Experience with human-in-the-loop workflows, hallucination mitigation, bias/fairness assessment, AI safety, and responsible AI practices. Preferred Qualifications Bachelor's or Master's degree in Computer Science, Engineering, Data Science, Artificial Intelligence, or a related field. Experience delivering enterprise AI solutions within complex, regulated environments. Strong stakeholder management, architectural documentation, technical decision-making, and cross-functional leadership skills. #J-18808-Ljbffr