The AI Governance & Delivery Lead defines and operationalizes the AI delivery model for the program and provides technical architecture leadership for enterprise AI solutions. The role ensures AI is applied consistently, safely, and effectively across the software development lifecycle, with a focus on production delivery value rather than theoretical experimentation.
This role establishes AI solution architecture standards, BMAD or comparable AI-native delivery practices, governance checkpoints, evaluation frameworks, measurable delivery outcomes, and Responsible AI guardrails while enabling delivery teams to adopt AI at scale.
This is a technical leadership role in an AI-first software engineering organization. Candidates must demonstrate production experience designing GenAI, RAG, and agentic solutions, and applying AI-assisted engineering to improve productivity, quality, and delivery outcomes.

Your future duties and responsibilities

- Define and govern the AI-enabled Software Development Lifecycle (AI SDLC) across the program.
- Support implementation of AI SDLC practices and Breakthrough Method of Agile AI driven Development (BMAD) within the delivery teams.
- Develop reusable templates, prompt libraries, patterns, and governance checkpoints for AI-assisted delivery.
- Ensure Responsible AI practices, human-in-the-loop controls, and auditability are embedded in delivery.
- Monitor AI-related delivery risks, including privacy, IP, security, bias, and model misuse.
- Measure AI adoption, productivity uplift, quality outcomes, and continuous-improvement opportunities.
- Coach delivery teams on practical AI engineering, prompt engineering, and AI-enabled ways of working.
- Partner with architecture, engineering, and quality teams to embed AI safely into delivery execution.
- Lead AI solution design reviews, document architectural decisions, and mentor teams on secure, scalable AI implementation.
- Define reference architectures for enterprise GenAI and agentic solutions, covering LLM integration, APIs, cloud deployment, state management, orchestration, and tool access (including MCP where applicable).
- Architect and govern RAG pipelines, including ingestion, parsing, chunking, embeddings, vector indexing, hybrid retrieval, reranking, document lifecycle, data lineage, and authorization-aware retrieval.
- Establish evaluation and quality standards for RAG and agents, including golden datasets, retrieval relevance, groundedness, tool-call correctness, regression testing, and production monitoring.
- Define enforceable AI security patterns for prompt-injection resilience, sensitive-data protection, agent identities, least-privilege tool access, tenant isolation, and audit trails.
- Establish LLMOps standards for model/prompt versioning, tracing, observability, latency, token/cost optimization, and deployment controls.
- Embed spec-driven engineering controls across requirements, specifications, generated code, tests, and CI/CD, with traceability and risk-based human approvals.

Required qualifications to be successful in this role

- 10+ years of experience in enterprise delivery, technology transformation, or software leadership.




- Proven experience establishing AI delivery operating models or AI governance practices.
- Hands-on experience with AI SDLC and practical application of AI in software delivery.
- Experience with the BMAD framework or a comparable AI-native delivery methodology.
- Strong knowledge of Responsible AI, model risk management, and human-in-the-loop controls.
- Strong understanding of enterprise integration, APIs, microservices, event-driven architectures, and distributed systems.
- Experience developing standards, policies, templates, and delivery guidance for technical teams.
- Demonstrated hands-on experience architecting and delivering production-grade GenAI applications, including LLMs, RAG, and agentic workflows; able to explain design trade-offs and troubleshoot implementation challenges.
- Experience leading or influencing multiple Agile delivery teams.
- Experience with public cloud environments, with Microsoft Azure preferred.
- Strong leadership, communication, and stakeholder-management skills.
- Experience in regulated or mission-critical enterprise environments is an asset.
- 5+ years of solution or software architecture experience, with direct technical involvement in production AI solution design.
- Strong knowledge of agentic architecture and orchestration trade-offs: deterministic workflows versus single/multi-agent systems, tool calling, state, identity, and secure execution.
- Practical understanding of RAG internals and design choices, including parsing/chunking, embedding-model selection, retrieval/reranking, evaluation, freshness, versioning, and access controls.
- Hands-on familiarity with an agent orchestration framework (e.g., LangGraph, Semantic Kernel, Microsoft Agent Framework, Google ADK, or equivalent); framework-specific products are not mandatory.
- Experience defining automated LLM/RAG/agent evaluation, AI application observability, and quality gates for production releases.
- Ability to review or prototype AI application code in Python or Java and guide engineering teams through technical issues.
- Understanding of AI-specific security threats and implementation controls, including prompt injection, tool misuse, identity propagation, and sensitive-data handling.
- Experience applying spec-driven or equivalent AI-native SDLC practices with requirements-to-code/test traceability and measurable delivery outcomes.

Preferred Qualifications

- Experience leading an AI Centre of Excellence or similar AI governance function.
- Experience with advanced RAG approaches (e.g., multimodal or agentic RAG, knowledge graphs, or multi-hop retrieval).
- Experience integrating AI capabilities into enterprise software engineering environments.
- Experience within telecom or other regulated industries.
- Experience with security,



compliance, and privacy-oriented delivery models.
- AI, cloud, agile, or architecture certifications are an asset.
- Experience evaluating new AI tools and delivery automation capabilities.
- Experience with MCP, agent-to-agent communication, and secure enterprise tool integration.
- Experience with Azure AI Foundry, Azure OpenAI, Azure AI Search, or comparable AWS/GCP platforms, including custom engineering beyond out-of-the-box configurations.
- Experience with evaluation/observability tools such as RAGAS, DeepEval, Promptfoo, LangSmith, Braintrust, or equivalent.
- Familiarity with NIST AI RMF, ISO/IEC 42001, AI red teaming, and risk-based governance automation.
- Experience with AI FinOps, including model routing, caching, token usage, inference cost, and latency optimization.

CGI is providing a reasonable estimate of the pay range for this role. The determination of this range includes factors such as skill set level, geographic market, experience and training, and licenses and certifications. Compensation decisions depend on the facts and circumstances of each case. A reasonable estimate of the current range is $135,000–$185,000. This role is an existing vacancy.

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📌 AI Architect — Governance & Delivery (Old Toronto)
🏢 CGI
📍 Old Toronto