Senior AI Platform Architect – Enterprise GenAI, GitHub Copilot & Azure

Job summary

Assess the existing implementation and define the target architecture, transition roadmap, and prioritized MVPs for a secure, modular AI-enhanced software engineering platform. Design and productionize integrations and capabilities for governed LLM access, enterprise knowledge retrieval, workflow orchestration, security, evaluation, governance, and observability, while documenting designs and enabling knowledge transfer.

Job details

Role: Senior AI Platform Architect – Enterprise GenAI, GitHub Copilot & Azure Location: Canada, Remote Duration: Long Term Role Summary / Mission Design and build AI-Enhanced Software Engineering Platform to accelerate software development and improve developer effectiveness, code quality, and engineering consistency. This is a hands-on platform design and implementation role, not an AI-model development or data-science role. The resource will prioritize pragmatic, high-value MVPs and develop reusable, modular, plug-and-play capabilities that integrate across evolving engineering toolchains. Key Responsibilities Assess the current implementation, define target architecture, transition roadmap, and appropriately scoped MVPs based on value, risk, urgency, and implementation effort. Prepare architecture, detailed design, security, data-flow, integration, deployment, and operational-support documentation. Present designs and obtain alignment from Information Security, Architecture Review Board, AI Centre of Excellence, Cloud Engineering, Privacy, Risk, and other stakeholders. Design and implement modular platform capabilities for governed LLM access, context engineering, enterprise knowledge retrieval, workflow orchestration, security, evaluation, governance, and observability. Develop and productionize prioritized engineering productivity use cases, initially integrating with GitHub Copilot, Azure DevOps repositories, work items, pipelines, service hooks, and enterprise knowledge sources. Design tool-agnostic interfaces supporting the planned transition to GitLab for repositories, CI/CD and security scanning, and Jira and Confluence for requirements and project documentation. Pair with internal engineers to create reference implementations, standards, architecture decisions, runbooks, and knowledge transfer Required Experience & Skills Enterprise AI platform engineering: Hands-on experience designing and productionizing secure, reusable enterprise AI platforms using managed foundation-model services, rather than developing or training AI models. GitHub Copilot and governed model access: Experience with GitHub Copilot enterprise capabilities, model selection, licensing and usage controls, and Microsoft-managed access to approved LLMs. Ability to design platform integrations that comply with approved model-access patterns and avoid unmanaged direct connections to public AI providers. Context engineering: Strong knowledge of prompt and context design, token management, structured outputs, tool calling, workflow state, grounding, citations, provenance, and prompt and model versioning. RAG and vector retrieval: Hands-on experience with embeddings, chunking, metadata design, semantic and hybrid search, retrieval evaluation, and technologies such as Azure AI Search, PostgreSQL with pgvector, or equivalent. Toolchain integration and portability: Strong experience with APIs, webhooks, abstraction layers, Azure DevOps, GitLab, Jira, Confluence, CI/CD, repositories, branch and pull-request controls, SAST, DAST, and software-composition analysis; able to design replaceable, loosely coupled components. Knowledge architecture: Experience modelling enterprise knowledge, taxonomies, entity relationships, metadata, and provenance using knowledge-graph or graph technologies where justified by the use case. AI gateway and model access: Experience implementing governed access to multiple approved LLMs, including model routing, provider abstraction, authentication, policy enforcement, retries, rate limits, token and cost monitoring, model allowlisting, safety controls, and telemetry. Security and observability: Strong experience with Microsoft Entra ID, managed identities, Azure Key Vault, least-privilege access, sensitive-data controls, prompt-injection defences, content safety, secure tool execution, audit logging, and OpenTelemetry or Application Insights.

What you’ll do

Assess the existing implementation and define the target architecture, transition roadmap, and prioritized MVPs for a secure, modular AI-enhanced software engineering platform. Design and productionize integrations and capabilities for governed LLM access, enterprise knowledge retrieval, workflow orchestration, security, evaluation, governance, and observability, while documenting designs and enabling knowledge transfer.

Requirements

Requires hands-on experience designing and productionizing secure enterprise AI platforms using managed foundation-model services, with expertise in GitHub Copilot, governed model access, context engineering, RAG, and vector retrieval. Candidates should also have strong toolchain integration and portability experience, plus knowledge of AI gateways, enterprise security controls, and observability in Azure environments.

Listed skills

• Microsoft Azure · Preferred
• Jira · Preferred

Other relevant skills

Identified from the job description. Confirm important requirements above.

• Enterprise AI Platform Engineering
• GitHub Copilot
• Azure
• Governed LLM Access
• Context Engineering
• Retrieval-Augmented Generation
• Vector Search
• Azure AI Search
• Toolchain Integration
• Azure DevOps
• GitLab
• Jira
• Confluence
• AI Gateway Design
• Microsoft Entra ID
• OpenTelemetry

Job areas

• Technology
• Software
• Engineering
• Security & Safety
• Consulting

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