Senior AI Developer - Product Operations
New
J
JobgetherAI software
Based in CanadaFull-TimeSenior
SalaryBase salary of CAD $140,000–$170,000 annually, with final compensation adjusted according to the candidate's province or region and local market conditions.
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Job Details
- Required Skills
- Content managementSlack
Requirements
- Bring strong production engineering experience across the AI agent development lifecycle.
- Have hands-on experience designing and deploying multi-agent systems in production.
- Have experience with orchestration, tool integration, context management, and memory.
- Have experience integrating AI agents with Slack, Atlassian products, and the Model Context Protocol (MCP).
- Be able to instrument and evaluate non-deterministic systems using evaluations, observability, regression testing, and cost tracking.
- Apply architectural judgment across reliability, security, and cost optimization, including selecting appropriate models for use cases.
- Form, communicate, and defend technical recommendations.
- Have experience building reusable components, internal tools, or technical capabilities adopted by other teams or users.
- Understand product workflows and translate Product Managers’ needs into practical technical solutions.
- Use AI-assisted development tools regularly and assess their strengths and limitations critically.
- Nice to have: product management experience or experience working on B2B SaaS, HR, or IT products.
Responsibilities
- Design, build, and deploy production AI agents with Product Managers, starting with product brief creation and the handoff to design.
- Expand agent capabilities across customer insights, planning, delivery, and measurement.
- Define shared architecture, technical standards, and reusable components for orchestration, skills, memory, evaluations, and guardrails.
- Establish production practices for observability, testing, security, reliability, and cost management, with human approval at critical decision points.
- Work with Product Managers to understand workflows, identify automation opportunities, and deliver iterative solutions for users to test and adopt.
- Measure agent effectiveness using indicators such as time saved, speed from customer feedback to product decisions, and cost per agent or feature.
- Evaluate solutions, prioritize measurable improvements, and discontinue initiatives that do not deliver value.
- Share agent development practices, tools, and technical knowledge with colleagues.
- Partner with AI engineering and data specialists to align architecture and share reusable patterns.
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