Senior Software Engineer - AI

Accountabilities: Lead projects end to end, translating specifications and loosely defined business problems into clear requirements, implementation plans, and production launches. Consistently deliver working software to production through frequent, incremental releases. Design, build, and deploy AI agents and AI-powered features for both customer-facing products and internal teams. Develop and extend the enterprise context layer, including data connectors, retrieval systems, permission enforcement, and Model Context Protocol (MCP) access. Build shared tooling, frameworks, and engineering practices that help teams across the organization develop and deploy AI agents effectively. Evaluate whether a problem is best addressed through an AI agent or a deterministic automated workflow, and implement the most appropriate solution. Provision and manage the infrastructure supporting applications, including environments, deployment pipelines, and production releases. Take long-term ownership of deployed systems, including monitoring, evaluation, cost management, reliability, and failure handling. Use tests, continuous integration, and agent-based review practices to maintain software quality while working with coding agents. Break down development tasks into manageable work that coding agents can execute and engineers can verify, coordinating multiple agents in parallel where appropriate. Mentor engineers within the AI Platform team and across the wider engineering organization. Collaborate with product, support, and engineering stakeholders to identify opportunities and deliver practical technical solutions. Requirements Demonstrated experience as a senior software engineer, with a strong track record of frequently shipping production software and maintaining ownership after release. Proven experience delivering AI agents or AI-powered features to production that are actively used and relied upon by users. Strong proficiency in Python and/or TypeScript. Advanced experience with agentic software development lifecycle (Agentic SDLC), using coding agents as a primary method of building software. Hands-on experience with large language model (LLM) APIs, MCP, and agent frameworks, including sound judgment about when an agent-based approach is appropriate and when it is not. Experience designing and implementing retrieval systems, including search, Retrieval-Augmented Generation (RAG), and permission-aware data access. Familiarity with evaluation frameworks and data-driven methods for determining whether AI agents are ready for production and performing effectively. DevOps and infrastructure experience, including Docker, Kubernetes or managed container platforms, infrastructure as code (Terraform or equivalent), and CI/CD pipeline development. Experience operating production services, including observability tools such as Datadog or similar platforms, alerting, secrets and identity management, and incident response. Strong SQL skills and practical experience with PostgreSQL. Excellent communication skills, including the ability to communicate effectively in live discussions, work independently, and be transparent about technical limitations or knowledge gaps. Strong problem-solving skills, sound technical judgment, and the ability to take ownership in a fast-moving engineering environment. Preferred qualifications: Experience or knowledge of fleet operations, transportation, or logistics. Experience with Databricks, including Unity Catalog or Genie. Familiarity with enterprise search integrations and connectors such as Confluence, SharePoint, Jira, and Slack. Experience with vector databases and retrieval infrastructure. Knowledge of identity and access control for services and AI agents. Experience with workflow orchestration or automation platforms. Familiarity with cloud environments such as Azure (App Service, AKS, Azure DevOps) or AWS. Benefits Remote flexibility: Fully remote position for candidates based in Canada. Technical ownership: Opportunity to lead projects and maintain long-term responsibility for production systems. AI-first environment: Work with coding agents and modern AI development practices as a core part of the engineering workflow. Broad technical scope: Build AI products, enterprise data infrastructure, automated workflows, and shared engineering platforms. Professional growth: Opportunity to shape agentic engineering practices and mentor engineers across teams. Meaningful impact: Develop technology that supports fleet safety, regulatory compliance, and operational efficiency. Inclusive workplace: Equal opportunity environment that values diverse perspectives and individual differences. Potential onsite travel: Willingness to travel to Edmonton, Alberta, as needed. Specific salary, healthcare coverage, paid leave, and additional compensation details are not provided in the job description.