AI Solutions Architect

Role overview

Architecture and delivery

• Own end-to-end architecture for AI-native applications and modernization, including components, interfaces, integrations, and model selection.
• Translate business needs into scalable, secure, maintainable solutions; validate architecture through prototypes, proofs of concept, and reference implementations.
• Build reference applications with C#, ASP.NET, .NET, Python, and Azure, including monitoring, logging, and observability.
• Develop reusable architecture documents, decision records, technical playbooks, standards, reference architectures, and operational runbooks.

AI engineering and lifecycle

• Design RAG pipelines, including embeddings, retrieval, reranking, and vector storage; evaluate Azure AI Search, Cosmos DB, pgvector, and Qdrant.
• Implement agentic AI and multi-agent orchestration using frameworks such as Semantic Kernel, AutoGen, CrewAI, LangGraph, LangChain, and LlamaIndex.
• Integrate Azure OpenAI, OpenAI APIs, and open-source models; define prompt engineering, versioning, memory, task chaining, and evaluation approaches.
• Operationalize AI-assisted development tools and workflows, including coding assistance, automated testing, code review, and developer productivity improvements.
• Define CI/CD, deployment, quality gates, release controls, rollback procedures, change management, security, and production readiness.
• Establish AI evaluation, regression testing, red-teaming, safety testing, observability, tracing, and AI-generated code review controls.

Governance, adoption, and leadership

• Ensure privacy, security, data residency, regulatory, and responsible AI compliance, collaborating on AI-specific threat modeling.
• Measure velocity, defect density, automation coverage, and delivery costs; present pilot results, risks, benefits, and scaling recommendations.
• Deliver a reusable AI-native SDLC adoption framework with playbooks, toolchain configurations, governance checkpoints, security controls, and deployment checklists.
• Provide technical leadership, architecture guidance, code reviews, and mentorship to developers and engineering teams.

Qualifications

• Undergraduate degree in Computer Science or related STEM field, or equivalent education and experience.
• 12+ years of progressive software development, solution architecture, or systems architecture experience, including recent hands-on LLM and AI integration delivery.
• Production delivery experience with AI applications, LLMs, RAG pipelines, and agentic systems, from prototype through stable deployment.
• Strong Python and C#/.NET Core development, object-oriented design, and modern software engineering experience.
• Experience with enterprise architecture, engineering standards, architecture documents, technical playbooks, and reference architectures.
• Experience with AI/ML tools, agentic frameworks, RAG and GraphRAG, embeddings, vector search, and vector databases.
• Experience with AI evaluations, regression tests, quality gates, and AI-driven testing; expertise in Azure cloud-native solutions, Azure OpenAI, CI/CD, and MLOps.
• Understanding of APIs, Git, Agile, enterprise integration, AI security, privacy, responsible AI, data governance, and regulatory compliance.
• ASP.NET and .NET 8/10, AI coding assistants, evaluation platforms, observability tools, IaC, regulated-industry experience, and listed certifications are preferred or assets.

Work arrangement

• The role is hybrid in the Greater Vancouver Metropolitan Area.

Contract

• The description states a 12-month contract.