AI Solution Architect

Job Title: AI Solution Architect
Job ID: 90846
Location: Vancouver, British Columbia - Remote 

Overview:
We are looking for a senior AI Solution Architect to lead the architecture and delivery of secure, scalable, responsible, and production-ready AI solutions. You will work across business, product, data science, engineering, security, privacy, risk, legal, operations, and vendor teams to translate business opportunities into practical AI solutions. You will guide initiatives from use-case discovery and experimentation through production, ensuring solutions deliver measurable value while meeting quality, security, privacy, responsible-AI, and operational requirements. This is a hands-on architecture role, not a governance-only position. You should be comfortable evaluating AI models and platforms, reviewing technical designs, validating prototypes and evaluation results, and making architecture decisions in ambiguous environments.

What you will be doing:

• Own end-to-end AI solution architecture from discovery and feasibility through production and ongoing optimization.
• Translate business needs into AI use cases, solution patterns, decision boundaries, acceptance criteria, and measurable outcomes.
• Design solutions using appropriate patterns such as generative AI, RAG, predictive ML, intelligent automation, AI agents, and intelligent document processing.
• Define architectures spanning models, data, knowledge retrieval, prompts, orchestration, APIs, applications, cloud, identity, security, and human oversight.
• Evaluate AI models, platforms, vendors, and build-vs-buy options based on quality, safety, cost, performance, data requirements, control, and strategic fit.
• Establish evaluation and release criteria covering accuracy, relevance, groundedness, hallucination, robustness, latency, safety, and cost.
• Guide responsible-AI, privacy, security, data-governance, model-risk, and regulatory considerations throughout the AI lifecycle.
• Lead architecture reviews and provide technical assurance from experimentation through production release and material changes.
• Guide MLOps/LLMOps, observability, deployment, monitoring, rollback, and operational-readiness requirements.
• Create reusable AI architecture patterns, guardrails, and reference approaches.
• Partner closely with engineers, data scientists, product leaders, and business stakeholders to resolve complex cross-functional challenges.
• Clearly communicate technical trade-offs, risks, limitations, and recommendations to both technical and non-technical audiences.

• 10+ years of relevant technology experience, including significant architecture responsibility and demonstrated AI/ML solution delivery.
• Proven experience designing and delivering production AI or machine-learning solutions.
• Strong end-to-end architecture experience across models, data, applications, integration, cloud, security, and operations.
• Deep understanding of generative AI, foundation models, RAG, embeddings, vector search, prompt orchestration, tool use, and agent patterns.
• Practical understanding of classical machine learning, model development lifecycles, data pipelines, evaluation, and common model failure modes.
• Experience with cloud AI services, model hosting, APIs, containers, identity, networking, and scalable deployment.
• Experience with MLOps/LLMOps, model evaluation, observability, release management, rollback, and monitoring.
• Working knowledge of responsible AI, AI security, privacy engineering, data governance, model risk, and human oversight.
• Ability to critically assess model/vendor documentation, APIs, prompts, evaluation results, data lineage, and implementation evidence.
• Strong stakeholder management, facilitation, communication, and decision-making skills.
• Bachelor's degree in computer science, engineering, data science, or a related discipline, or equivalent experience.

• Experience delivering AI solutions in financial services or another regulated industry.
• Experience with Azure AI, Azure Machine Learning, Azure OpenAI, or comparable cloud AI platforms.
• Knowledge of model gateways, vector databases, knowledge graphs, model registries, AI safety tools, and AI observability platforms.
• Experience evaluating commercial AI products, embedded copilots, SaaS solutions, managed AI services, and open-source/open-weight models.
• Familiarity with enterprise architecture, AI risk, security, and governance frameworks.
• Relevant cloud, AI/ML, data, security, or architecture certifications.

• AI initiatives start with clear business outcomes and measurable success criteria.
• Solutions move from experimentation to production only when value, quality, safety, control, and operational-readiness requirements are met.
• AI, data, privacy, security, legal, and responsible-AI risks are identified and managed early.
• Production AI is observable, reliable, secure, and cost-aware.
• Delivery teams have clear architecture guidance, guardrails, and evaluation criteria.
• Reusable AI capabilities and patterns reduce duplicated effort and improve consistency.
• AI solutions can be effectively supported, governed, monitored, and evolved throughout their lifecycle.