Position overview

As the AI Solution Architect, you will have the mandate to support the solution architecture for the shared AI foundations that power intelligent applications and agents across Bombardier. You will upgrade the enterprise context layer while establishing MLOps and AI engineering practices at scale. This is a senior, hands-on solutioning role: you will set direction, create reference implementations, review designs, and work directly in codebases to resolve complex technical issues and unblock delivery teams. 

What are your contributions to the team?

• Own the target solution architecture, roadmaps, ensuring standards and reusable patterns are applied for the enterprise AI context layer that powers agents and AI-enabled applications.
• Design how agents securely access and combine enterprise data, documents, APIs, semantic models, vector indexes, knowledge graphs, ontologies, memory, tools, and identity-aware context.
• Implement reference architectures and implementation patterns for retrieval-augmented generation (RAG), GraphRAG, semantic search, context engineering, agent orchestration, tool use, and human-in-the-loop controls in collaboration with the Domain Architect.
• Define and Implement the enterprise approach to MLOps and GenAIOps, including source control, automated testing, evaluation, model and prompt versioning, CI/CD, deployment promotion, monitoring, observability, rollback, and production support.
• Create and maintain architecture decision records, technical standards, reusable components, starter repositories, APIs, and sample implementations that accelerate delivery across teams, and, present the decisions at the various enterprise architecture forums.
• Remain hands-on in Python, TypeScript, C#, .NET, SQL, or equivalent enterprise technologies; diagnose difficult issues, review code, build targeted proofs of concept, and help teams unblock programming challenges.
• Guide solution development teams from experimentation to secure, scalable, supportable, and cost-conscious production implementations.
• Partner with domain architecture, cybersecurity, data governance, platform engineering, cloud, application teams, and business stakeholders to align solutions with enterprise standards and regulatory obligations.
• Define quality gates and non-functional requirements for reliability, performance, privacy, security, resilience, interoperability, maintainability, and responsible AI.
• Support evaluation of technologies and vendors, guide build-versus-buy recommendations, and translate emerging capabilities into pragmatic enterprise patterns.
• Mentor developers, lead technical design reviews, and raise engineering capability through coaching, documentation, and communities of practice.
• Communicate complex architecture decisions clearly to technical teams, leadership, and business stakeholders, including trade-offs, dependencies, risks, and implementation paths. 

How to thrive in this role?

• You hold a Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, Data Engineering, Artificial Intelligence, or a related field.
• You have more than 8 years of professional experience in software engineering, cloud architecture, solution architecture, data platforms, or AI/ML, including significant experience with production-grade enterprise systems.
• You possess over 3 years of experience defining solution or platform architectures while remaining hands-on with software design, coding, debugging, and technical delivery.
• You hold strong programming proficiency in Python and at least one additional enterprise language such as TypeScript, C#, Java, or .NET.
• You have proven experience implementing MLOps, DevOps, or GenAIOps practices at scale using Git, automated testing, CI/CD, infrastructure as code, monitoring, and release governance.
• You possess experience designing AI-enabled applications leveraging LLMs, RAG, agents, vector search, model evaluation, prompt orchestration, and responsible AI controls.
• You have strong knowledge of enterprise data integration and context engineering, including APIs, event-driven architectures, relational and NoSQL databases, metadata, semantic models, and document repositories.
• You have experience building, querying, or maintaining production knowledge graphs and ontologies using technologies such as RDF, OWL, SKOS, SHACL, SPARQL, Cypher, Gremlin, R2RML, or equivalent approaches.
• You possess hands-on experience with Microsoft Azure and Azure DevOps, along with familiarity with Azure AI Services, Azure OpenAI, Microsoft Graph, Databricks, or similar enterprise platforms.
• You have a strong understanding of secure software development, identity and access management, authorization, secrets management, data protection, logging, observability, resilience, and production support.
• You have the ability to structure ambiguous technical challenges, make sound architectural decisions, and balance strategic objectives with practical delivery constraints.
• You possess excellent communication, facilitation, and mentoring skills, enabling you to influence stakeholders across architecture, engineering, data, security, operations, and business functions.
• You have the ability to communicate effectively in both French and English within a business environment.