AI Solution Architect

Position overview

Architecture and delivery

• Own target architecture and roadmaps for the enterprise AI context layer and establish standards and reusable patterns.
• Design secure agent access to enterprise data, documents, APIs, semantic models, vector indexes, knowledge graphs, ontologies, memory, tools, and identity-aware context.
• Implement patterns for RAG, GraphRAG, semantic search, context engineering, agent orchestration, tool use, and human-in-the-loop controls.
• Define enterprise MLOps and GenAIOps practices covering source control, testing, evaluation, versioning, CI/CD, monitoring, observability, rollback, and production support.
• Maintain architecture decisions, standards, reusable components, starter repositories, APIs, and sample implementations; present decisions at architecture forums.
• Work in codebases, diagnose issues, review code, build proofs of concept, and unblock teams using enterprise technologies.
• Guide teams from experimentation to secure, scalable, supportable, cost-conscious production systems.
• Partner across architecture, security, data governance, platform, cloud, application, and business teams to meet standards and regulatory obligations.
• Set quality gates for reliability, performance, privacy, security, resilience, interoperability, maintainability, and responsible AI.
• Evaluate technologies and vendors, advise on build versus buy, mentor developers, lead reviews, and communicate decisions and trade-offs.

Education and experience

• Bachelor's or Master's degree in Computer Science, Software Engineering, Information Technology, Data Engineering, Artificial Intelligence, or a related field.
• More than 8 years in software engineering, cloud or solution architecture, data platforms, or AI/ML, including significant production enterprise systems experience.
• More than 3 years defining solution or platform architectures while staying hands-on in software design, coding, debugging, and delivery.

Technical qualifications

• Strong Python proficiency and at least one additional enterprise language, such as TypeScript, C#, Java, or .NET.
• Experience implementing MLOps, DevOps, or GenAIOps at scale with Git, automated testing, CI/CD, infrastructure as code, monitoring, and release governance.
• Experience designing AI applications using LLMs, RAG, agents, vector search, model evaluation, prompt orchestration, and responsible AI controls.
• Knowledge of APIs, event-driven architectures, relational and NoSQL databases, metadata, semantic models, and document repositories.
• Experience with production knowledge graphs and ontologies; examples include RDF, OWL, SKOS, SHACL, SPARQL, Cypher, Gremlin, and R2RML.
• Hands-on Microsoft Azure and Azure DevOps experience; familiarity with Azure AI Services, Azure OpenAI, Microsoft Graph, Databricks, or similar platforms.
• Knowledge of secure development, access management, authorization, secrets, data protection, logging, observability, resilience, and production support.
• Able to structure ambiguous technical challenges and balance strategic objectives with practical delivery constraints.
• Excellent communication, facilitation, and mentoring skills; able to communicate effectively in both French and English in a business environment.

About Bombardier

Position details