Senior Forward Deployed Engineer (AI)
New
CanadaFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 4+ years
- Required Skills
- Artificial IntelligenceCloud ComputingFull Stack DevelopmentProduct Management
Requirements
- 4+ years of professional experience shipping production software end to end, including AI-enabled or AI-integrated applications.
- Strong ability to translate unclear business needs into structured product and technical requirements.
- Full-stack engineering expertise across frontend, backend, and data systems.
- Hands-on experience with agentic coding tools such as Claude Code, Cursor, or Augment.
- Experience deploying and operating applications on a major cloud platform.
- Understanding of application security principles, especially for systems handling sensitive organizational data.
- Strong product judgment with ability to scope MVPs, prioritize features, and define clear success criteria.
- Excellent written and verbal communication skills for both technical and non-technical audiences.
- Proven ability to collaborate directly with stakeholders.
Responsibilities
- Partner with cross-functional business teams to uncover and prioritize opportunities where AI can improve workflows.
- Lead end-to-end product delivery, including discovery, requirements definition, prototyping, development, deployment, and production rollout.
- Translate ambiguous business problems into clear technical specifications, acceptance criteria, and scalable AI-driven solutions.
- Build and maintain full-stack internal applications using modern AI tools and cloud infrastructure.
- Use agentic coding environments as primary development tools to accelerate engineering workflows.
- Manage deployment, adoption, and post-launch iteration based on real user feedback and performance data.
- Ensure appropriate engineering standards around reliability, security, and maintainability for internal systems handling sensitive data.
- Produce documentation, training materials, and rollout plans to drive successful adoption across teams.
- Contribute reusable components, best practices, and technical learnings back to broader AI engineering initiatives.
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