AI Solutions Architect (Senior)
On behalf of our client, Procom is seeking an AI Solutions Architect (Senior) for a 12-month engagement in Richmond, BC
AI Solutions Architect (Senior) Job Details
Our client is seeking an AI Solution Architect to define the architecture and set the technical direction for AI-Native projects.
This is a senior, architecture-led role that remains hands-on and credible at the code level, providing technical leadership to the cross-functional engineering team that designs, builds, and deploys production-grade application rewrites using AI-native tools and techniques on the Azure platform.
Working with AI SDLC team and reporting directly to the AI Operations Manager, this role owns the end-to-end solution architecture - from requirements intake through production deployment - and is accountable for demonstrating that an AI-native SDLC can drive higher velocity, greater delivery confidence, and accelerated time to market relative to the current lifecycle.
The Architect sets engineering standards, selects and operationalizes the AI development toolchain, validates key decisions through prototypes and reference implementations, and produces the reusable playbooks and reference architectures adopted across the portfolio.
As the principal architectural authority for the initiative, the successful candidate brings deep solution and systems architecture experience on top of strong, current engineering ability. They provide technical direction and mentorship to senior developers and engineers, challenge existing approaches, and set a high standard for both architecture and broader team engineering practices.
Detailed Responsibilities and Deliverables:
- Own the end-to-end solution architecture for AI-native applications and application rewrites, defining logical components, interfaces, integration patterns, and foundational model selection (LLMs/SLMs).
- Architect and introduce an end-to-end AI-Native SDLC playbook covering the full lifecycle from requirements intake through production deployment, documenting methodology, roles, decision points, trade-offs, lessons learned, and reusable templates.
- Author Architecture Definition Documents (ADDs), reference architectures, and engineering standards (ADRs, runbooks) that guide the delivery team's implementation.
- Translate business objectives into solution designs and validate architectural feasibility through hands-on prototyping, proof-of-concept builds, and reference implementations.
- Build reference implementations using C#, ASP.NET, .NET 10, Python, and Azure with monitoring, logging, and observability, establishing the patterns the engineering team scales to production.
- Architect and build reference RAG pipelines (embeddings, retrieval, re-ranking) and define vector storage solutions (e.g., Azure AI Search, Cosmos DB, pgvector, Qdrant) to meet latency, cost, and quality targets.
- Design and standardize agentic AI frameworks and multi-agent orchestration patterns (e.g., Semantic Kernel, AutoGen, CrewAI, LangGraph, LangChain, LlamaIndex) as reusable orchestration components.
- Integrate LLM and AI capabilities into enterprise applications using Azure OpenAI, OpenAI APIs, and open-source models, progressing reference solutions from prototype to production-ready release.
- Define prompt engineering strategies, prompt versioning, memory management, and task chaining with evaluation coverage using frameworks such as PromptFlow or Prompty.
- Select, configure, and operationalize the AI development toolchain, including IDE integration (e.g., VS Code, Visual Studio), AI coding assistants (e.g., GitHub Copilot, Cursor), agentic development tools (e.g., Copilot Agent Mode), AI-assisted code review, and developer workflow automation.
- Define and implement the release and deployment process, ensuring AI agents and AI-assisted development activities operate within existing enterprise guardrails, including change management, approvals, automated test gates, deployment controls, rollback procedures, and production readiness reviews.
- Integrate application quality, risk, and release-readiness controls into the pipeline, including static analysis, dependency scanning, secret detection, code quality checks, and review gates for AI-generated code.
- Design and govern AI evaluation and quality assurance processes using LLM eval frameworks (e.g., Azure AI Evaluation SDK, DeepEval), including automated regression suites, red-teaming, safety testing, and quality gates for AI-generated outputs.
- Establish AI observability and tracing using Azure Monitor, Application Insights, Dynatrace, LangSmith, and MLflow Tracing (OpenTelemetry) for end-to-end request logging, latency tracking, and trace correlation.
- Ensure solutions adhere to privacy, security, data residency, and ethical-AI regulatory requirements, collaborating with security teams on threat modeling unique to intelligent systems.
- Produce a measurable comparison of the AI-native SDLC against the current lifecycle, covering delivery velocity, defect density, automation coverage, and cost-to-deliver.
- Present pilot outcomes, quantified benefits, risks, and recommendations on scaling the AI-native SDLC methodology to senior stakeholders across future application rewrites.
- Deliver a reusable AI-Native SDLC adoption package, including playbook, reference architecture, toolchain configuration, delivery templates, governance checkpoints, security gates, and production deployment checklist.
- Provide technical direction, coaching, and code review to senior developers and engineers, mentoring the team and raising engineering standards across the portfolio.
AI Solutions Architect (Senior) Mandatory Skills
- Undergraduate degree in Computer Science or a related STEM (Science, Technology, Engineering or Math) discipline.
- 12+ years of progressive software development and solution/systems architecture experience, including recent hands-on delivery with LLMs or AI integration (an equivalent combination of education and experience may be considered).
- Proven experience designing AI-powered solutions (LLM/RAG/agentic systems) and transitioning them from prototype to stable production release, with strong current engineering ability on par with senior developers.
- Demonstrated experience defining engineering standards and authoring Architecture Definition Documents (ADDs), technical playbooks, and reference architectures.
- Strong development capability in Python and C#/.NET Core, with object-oriented design.
- Hands-on experience with agentic orchestration frameworks (e.g., Semantic Kernel, LangGraph, AutoGen, CrewAI) and AI/ML frameworks (e.g., OpenAI SDKs, LangChain, Hugging Face).
- Familiarity with RAG/GraphRAG, embeddings, and vector databases (e.g., Cosmos DB, pgvector, Qdrant, Azure AI Search).
- Experience developing and executing AI-driven tests (e.g., LLM evals, regression suites, and automated quality gates).
- Expert knowledge of designing and implementing cloud-ready solutions on Azure, spanning microservices, containers, CI/CD pipelines, and MLOps practices.
- Knowledge of APIs, Git, and Agile software development practices.
- Ability to synthesize complexity and communicate AI capabilities clearly to diverse audiences, including senior stakeholders.
- Strong collaboration and leadership skills within cross-functional teams.
AI Solutions Architect (Senior) Preferred Skills
- Azure Solutions Architect Expert or AWS Certified Solutions Architect, with demonstrated mastery of Azure OpenAI Service architectures, MLOps orchestration, and enterprise data landing zones.
- Experience with advanced agentic frameworks and multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen Studio, Semantic Kernel Agents).
- Hands-on use of AI coding assistants and agentic development tools (e.g., GitHub Copilot, Cursor) in production delivery.
- Experience with AI evaluation and benchmarking platforms (e.g., Azure AI Evaluation SDK, DeepEval).
- Familiarity with AI observability and tracing tools (e.g., Dynatrace, LangSmith, MLflow Tracing, Weights & Biases, PromptFlow).
- Experience with ASP.NET and .NET 8/10 for cloud-native web applications and APIs.
- Azure platform experience including Azure App Service, Azure Functions, Container Apps, Azure SQL, Azure Key Vault, Azure DevOps, and Azure Monitor.
- Experience with Infrastructure as Code (Terraform or Bicep).
- Experience in regulated industries (insurance, healthcare, government, financial services) where compliance, audit, and governance are required.
AI Solutions Architect (Senior) Assignment Length
12 Months
AI Solutions Architect (Senior) Assignment Location
Richmond, BC
