Software Development Manager, MCP/AI
New
CanadaFull-TimeManager
SalaryCAD 140,000–205,700 range, depending on experience
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Job Details
- Experience
- 7+ years of software engineering experience with at least 3+ years in a technical leadership or engineering management role.
- Required Skills
- AgileCI/CDRESTful APIsDistributed Systems
Requirements
- Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent practical experience.
- 7+ years of software engineering experience with at least 3+ years in a technical leadership or engineering management role.
- Proven experience delivering complex, production-grade distributed systems or platform services.
- Hands-on experience building or leading teams delivering AI/ML-powered products, including LLMs, agents, or recommendation systems.
- Strong understanding of LLMs, agentic workflows, and AI system evaluation methodologies.
- Solid background in cloud-native architectures, APIs, CI/CD, testing, and Agile development practices.
- Experience defining engineering standards and scaling teams in ambiguous or greenfield environments.
- Strong leadership and communication skills with the ability to influence across technical and non-technical stakeholders.
Responsibilities
- Build, lead, and scale a high-performing engineering team, including AI/ML talent, through hiring, coaching, and performance development.
- Define and drive the technical vision, architecture, and roadmap for a next-generation agentic AI and MCP-based platform.
- Own end-to-end delivery of Viewer MCP and AI-powered capabilities, ensuring predictable execution and high-quality releases.
- Establish engineering and AI best practices, including CI/CD, testing, observability, and production reliability for AI systems.
- Design and implement evaluation frameworks for AI quality, safety, latency, cost, and reliability, embedding them into delivery workflows.
- Partner cross-functionally with product, UX, applied AI, and platform teams to build a shared agentic layer across visualization solutions.
- Drive adoption of responsible AI practices, including human-in-the-loop design, traceability, and confidence scoring mechanisms.
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