Enterprise AI Architect

Job summary

Define enterprise AI architecture, standards, and governance, and partner with the AI Center of Excellence to prioritize use cases and design solutions. Oversee AI solution delivery and operations, integrate AI with enterprise systems, and provide technical guidance and architecture recommendations to teams and senior leaders.

Job details

Enterprise AI Architect (Contract, Remote, Canada) Location: Remote (Canada) Employment Type: Contract (6–12 months, extension possible) About the Role We're looking for an Enterprise AI Architect to work alongside our clients in-house AI Center of Excellence (CoE), under shared leadership. You'll act as a senior technical partner to the CoE, shaping the architecture, standards, and delivery of enterprise AI solutions from concept through production. You'll bring outside perspective and hands-on expertise to accelerate how we design, build, and deploy AI-driven processes at scale. What You'll Do • Define the enterprise AI reference architecture covering data, models, orchestration, security, and governance • Partner with the AI CoE to evaluate use cases, prioritize initiatives, and translate business needs into technical solution designs • Design and oversee development of AI solutions, including GenAI/LLM applications, RAG pipelines, agentic workflows, and ML models • Establish MLOps/LLMOps practices for deployment, monitoring, versioning, and lifecycle management • Evaluate and recommend AI platforms, foundation models, vector databases, and tooling (build vs. buy) • Ensure AI solutions meet security, privacy, compliance, and Responsible AI standards (including Canadian requirements such as PIPEDA) • Integrate AI capabilities into existing enterprise systems (ERP, CRM, data platforms, APIs) • Create architecture documentation, design patterns, and reusable accelerators for the CoE • Provide technical guidance and mentorship to CoE engineers, data scientists, and business stakeholders • Present architecture decisions, roadmaps, and trade-offs to senior leadership What You Bring • 10+ years in solution/enterprise architecture, with 3+ years designing and delivering AI/ML solutions in production • Hands-on experience with GenAI and LLMs (OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, Google Vertex AI, or open-source models) • Strong knowledge of RAG, vector databases, prompt engineering, agent frameworks, and evaluation methods • Experience with a major cloud platform (Azure, AWS, or GCP) and cloud-native architecture • Solid understanding of MLOps/LLMOps, CI/CD, containerization (Docker, Kubernetes), and infrastructure as code • Proficiency in Python and familiarity with modern data engineering stacks (Snowflake, Databricks, Spark, etc.) • Working knowledge of AI governance, model risk management, data privacy, and security best practices • Excellent communication skills, with the ability to work with both technical teams and executives • Proven ability to work independently and collaboratively with an internal team in a remote setting Nice to Have • Cloud or AI certifications (Azure AI Engineer, AWS ML Specialty, Google Professional ML Engineer, TOGAF) • Experience setting up or advising an AI Center of Excellence • Background in regulated industries (financial services, healthcare, insurance, public sector) • Experience with enterprise automation platforms or agentic AI orchestration tools

What you’ll do

Define enterprise AI architecture, standards, and governance, and partner with the AI Center of Excellence to prioritize use cases and design solutions. Oversee AI solution delivery and operations, integrate AI with enterprise systems, and provide technical guidance and architecture recommendations to teams and senior leaders.

Requirements

Requires 10+ years of solution or enterprise architecture experience, including 3+ years delivering production AI/ML solutions, with hands-on expertise in GenAI, LLMs, RAG, and cloud platforms. Candidates should also bring knowledge of MLOps/LLMOps, Python, data engineering, security, privacy, and AI governance, along with strong communication and remote collaboration skills.

Listed skills

• Kubernetes · Preferred
• Machine learning · Preferred
• Python · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Enterprise Architecture
• Artificial Intelligence
• Machine Learning
• Generative AI
• Large Language Models
• Retrieval-Augmented Generation
• Vector Databases
• MLOps
• LLMOps
• Cloud Architecture
• Python
• Data Engineering
• AI Governance
• Data Privacy
• Kubernetes
• Infrastructure as Code

Job areas

• Technology
• Software
• Data & Analytics
• Consulting
• Management & Leadership

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