Senior Software Engineer, AI Automation

CoalitionCyber insurance

Any location, CanadaFull-TimeSenior

Salary134,600 - 184,600 CAD per year

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Job Details

Have 7+ years of hands-on software engineering experience, including substantial recent experience building and shipping LLM-backed applications used by real users.
Have taken AI agents or multi-step automation workflows from prototype through reliable production operation.
Bring strong Python engineering skills and practical SQL expertise, including the ability to work directly with production data models.
Have deep experience with prompting, tool use, agentic loops, structured outputs, multi-step workflows, error handling, and production deployment.
Have experience designing and operating systems that integrate with business-critical systems of record.
Demonstrate judgment in evaluation methodology, reliability, safety controls, and responsible AI deployment tradeoffs.
Have production engineering fundamentals across Git, CI/CD, containers, deployment, monitoring, testing, permissions, and rollback.
Be able to lead technical work in ambiguous environments, influence stakeholders, and turn incomplete requirements into practical solutions.
Have clear written and verbal communication skills for working with technical and non-technical partners.
Experience with MCP servers, agent platforms, or reusable tool and skill frameworks is a bonus.
Familiarity with Linear, Jira, Zendesk, Salesforce, or Snowflake is a bonus.

Lead the technical design and delivery of AI agents, reusable skills, and automation workflows for internal teams.
Translate complex business processes, judgment calls, and edge cases into reliable, maintainable software.
Design and evolve integrations between agents and systems of record using MCP and comparable tool-integration frameworks.
Define architecture, interfaces, data contracts, and operational standards for production AI systems.
Establish evaluation strategies, quality baselines, and release criteria, and use results to guide deployment and iteration.
Build controls for permissions, observability, auditability, human-in-the-loop review, failure containment, and rollback.
Partner with data engineering and business stakeholders to ensure automations use trustworthy data and have sustainable ownership.
Drive technical decisions across workflows, identify reusable patterns, and mentor engineers and technical partners.
Document systems, decisions, and operating practices so teams can maintain and extend the work.

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