Software Engineer, AI Automation

Accountabilities

• Build and ship AI-powered applications that automate manual and repetitive business processes across multiple internal teams.
• Translate business processes, judgment calls, edge cases, and operational requirements into reusable skills that AI agents can reliably invoke.
• Design and implement agentic workflows that combine skills, tools, and systems of record into production-ready solutions.
• Connect agents to internal systems through Model Context Protocol (MCP) servers and expand MCP capabilities when required systems or data sources are not yet supported.
• Define success criteria and establish evaluation standards with business stakeholders before building or deploying AI solutions.
• Create evaluation datasets, establish performance baselines, and use objective evaluation results to determine whether an agent is ready for production.
• Take AI agents from proof of concept through production deployment, including permissions, human-in-the-loop checkpoints, audit trails, rollback mechanisms, and ongoing support.
• Monitor and improve agent behavior based on real-world usage, identifying failure modes and implementing appropriate safeguards and improvements.
• Create clear documentation and establish ownership with business teams so deployed agents can be operated effectively without continuous engineering support.
• Partner closely with data engineers to define requirements for data access, data models, data quality, and the information agents need to read or write.
• Work directly with non-technical teams to understand their workflows, identify automation opportunities, and convert operational knowledge into technical requirements.
• Challenge existing assumptions and proposed approaches using your practical experience with AI application development and agent architectures.
• Use modern agentic coding tools to accelerate development and navigate unfamiliar codebases efficiently.
• Maintain production-quality engineering practices across version control, continuous integration, containers, deployment, and operational support.
• Identify and resolve ambiguity, undocumented processes, fragmented systems, and unclear ownership to move automation initiatives forward.

Requirements

• You have at least 5 years of hands-on software development experience, with demonstrated practical experience building and shipping production software.
• You have substantial hands-on experience building applications powered by Large Language Models (LLMs), including prompting, tool use, agentic loops, and multi-step workflows.
• You have shipped at least one AI agent or LLM-powered application that non-engineering users depend on in their day-to-day work.
• You understand the practical limitations and failure modes of LLM-based systems and know how to design safeguards and controls around them.
• You have practical experience with agent tooling, including MCP, skill or tool authoring, and orchestrating agents against real systems of record.
• You have experience building evaluation datasets and using evaluation results to make practical decisions about AI system quality and production readiness.
• You are fluent in Python and comfortable working with SQL and production data models.
• You have experience using agentic coding tools such as Cursor to work efficiently within unfamiliar codebases.
• You understand core software delivery practices, including Git, continuous integration, containers, deployment, and production operations.
• You can work directly with non-technical business stakeholders to understand processes, identify requirements, and translate operational needs into reliable technical solutions without relying on a Product Manager to mediate every interaction.
• You are comfortable working in ambiguous environments where processes may be undocumented, internal tools may be imperfect, and ownership may not always be clearly defined.
• You demonstrate strong problem-solving skills and the ability to identify a path forward when requirements, data, or systems are incomplete.
• You are self-directed, pragmatic, and outcomes-focused, with the ability to take ownership from discovery through deployment and ongoing support.
• Experience building or maintaining MCP servers is considered a strong advantage.
• Previous experience in solutions engineering, professional services, internal tools, or startup founding environments is a plus.
• Familiarity with systems such as Linear, Jira, Zendesk, Salesforce, or Snowflake is beneficial.
• Experience in insurance, fintech, healthcare, or another regulated environment where technical errors can create compliance or business risks is a plus.

Benefits

• Base salary of $128,500–$160,600 per year for candidates located in Alberta, British Columbia, or Ontario.
• Base salary of $115,600–$144,540 per year for candidates located in other Canadian locations.
• Compensation is determined based on factors including relevant education, skills, qualifications, experience, credentials, and geographic location.
• 100% coverage for medical, dental, and vision benefits.
• Flexible Paid Time Off (PTO).
• Annual home office stipend and access to WeWork.
• Mental and physical wellness programs, including resources such as Headspace and Lumino.
• Opportunities for professional growth, advancement, and expanded technical ownership.
• Remote-first working environment designed to support collaboration across distributed teams.
• Opportunity to work on practical AI automation initiatives with organization-wide impact.
• Exposure to emerging AI technologies, agent architectures, evaluation methodologies, and production AI engineering.
• Opportunity to collaborate closely with engineering, data, and business teams while building solutions used by non-technical stakeholders.
• A role with significant individual ownership and the opportunity to shape how AI automation is developed and deployed across the organization.