AI Solutions Architect

Job Title: AI Solutions Architect
12-month contract | Hybrid in the Greater Vancouver Metropolitan Area | Must hold a valid Canadian Work Visa

Our client, a leading provincial organization dedicated to workplace health, safety, and injury prevention, is seeking an experienced Senior AI Solutions Architect for a 12-month hybrid contract in the Greater Vancouver area.
The Senior AI Solutions Architect will provide architectural leadership and technical direction for a portfolio of AI-native application development and modernization initiatives. This is a senior, architecture-focused role that requires strong hands-on engineering capabilities, combining enterprise solution architecture with the practical design, development, and deployment of production-grade AI-powered applications on Microsoft Azure.
Working closely with a cross-functional AI software development team, the successful candidate will own the end-to-end solution architecture, from requirements gathering and technical design through implementation, testing, and production deployment. A key focus will be establishing and operationalizing an AI-Native Software Development Lifecycle (SDLC) that leverages AI-assisted development tools, agentic workflows, automation, and modern engineering practices to accelerate delivery while maintaining quality, security, and governance standards.
The role will involve designing and implementing solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), agentic AI frameworks, multi-agent orchestration, and Azure AI services. The Architect will also establish reusable engineering standards, reference architectures, development playbooks, and quality assurance frameworks to support broader adoption across the organization.
The ideal candidate brings extensive experience in enterprise solution architecture and software engineering, with recent hands-on experience delivering AI-powered applications into production. This position requires someone who can operate as a principal architectural authority while remaining technically proficient at the code level, providing mentorship and guidance to senior developers and engineering teams.

Responsibilities:
Own the end-to-end solution architecture for AI-native applications and enterprise application modernization initiatives, defining logical components, interfaces, integration patterns, and foundational model selection
Establish and operationalize an AI-Native Software Development Lifecycle (SDLC) covering requirements intake, solution design, development, testing, deployment, and production support
Develop reusable technical playbooks, Architecture Definition Documents (ADDs), Architecture Decision Records (ADRs), reference architectures, engineering standards, and operational runbooks
Translate business objectives and requirements into scalable, secure, and maintainable technical solutions
Validate architectural decisions through hands-on prototyping, proof-of-concept development, and reference implementations
Design and build reference applications using C#, ASP.NET, .NET 10, Python, and Microsoft Azure, incorporating appropriate monitoring, logging, and observability capabilities
Architect and implement Retrieval-Augmented Generation (RAG) pipelines, including embeddings, retrieval strategies, re-ranking, and vector storage solutions
Evaluate and implement vector databases and search technologies such as Azure AI Search, Azure Cosmos DB, pgvector, and Qdrant
Design reusable agentic AI and multi-agent orchestration patterns using frameworks such as Semantic Kernel, AutoGen, CrewAI, LangGraph, LangChain, and LlamaIndex
Integrate LLMs and AI capabilities into enterprise applications using Azure OpenAI, OpenAI APIs, and open-source models
Define prompt engineering strategies, prompt versioning, memory management, task chaining, and evaluation approaches using tools such as PromptFlow or Prompty
Select, configure, and operationalize AI-assisted software development tools, including GitHub Copilot, Cursor, agentic development tools, and AI-enabled code review workflows
Establish standardized development workflows incorporating AI-assisted coding, automated testing, code review, and developer productivity improvements
Define and implement CI/CD pipelines, deployment processes, automated quality gates, release controls, and rollback procedures for AI-native applications
Ensure AI-assisted development activities comply with established enterprise change management, security, testing, and production readiness requirements
Integrate static code analysis, dependency scanning, secret detection, code quality checks, and review controls for AI-generated code
Design and implement AI evaluation and quality assurance frameworks, including LLM evaluations, automated regression testing, red-teaming, safety testing, and quality gates
Establish comprehensive AI observability and tracing capabilities using technologies such as Azure Monitor, Application Insights, Dynatrace, LangSmith, MLflow Tracing, and OpenTelemetry
Ensure solutions comply with privacy, security, data residency, regulatory, and responsible AI requirements, collaborating with security teams on AI-specific threat modeling
Evaluate the effectiveness of AI-native development practices against traditional delivery approaches, measuring development velocity, defect density, automation coverage, and delivery costs
Prepare and present pilot outcomes, measurable benefits, technical risks, and recommendations for scaling AI-native development practices
Deliver a reusable AI-Native SDLC adoption framework, including technical playbooks, reference architectures, toolchain configurations, governance checkpoints, security controls, and deployment checklists
Provide technical leadership, architecture guidance, code reviews, and mentorship to senior developers and engineers, promoting consistent engineering standards and best practices

Qualifications:
Undergraduate degree in Computer Science