Principal Software Development Engineer - Applied AI
New
J
JobgetherSoftware Development
CanadaFull-TimePrincipal
Salary200,000 - 220,000 CAD per year
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Job Details
- Experience
- 10+ years
- Required Skills
- PythonArtificial IntelligenceFull Stack DevelopmentMachine LearningMicroservicesDistributed Systems
Requirements
- 10+ years of professional software engineering experience with increasing technical impact and leadership responsibility.
- Several years of experience designing and operating production-level LLM, AI, or agentic systems at Staff Engineer level or above.
- Proven experience designing distributed systems and microservices for complex business domains.
- Strong understanding of architecture principles, including domain-driven design, clean architecture, and system performance.
- Broad full-stack engineering knowledge, including modern frontend technologies and Python-based backend systems.
- Experience creating architecture standards and technical decision documents adopted across teams.
- Demonstrated ability to evaluate AI platforms, vendors, and infrastructure solutions using measurable criteria and cost analysis.
- Deep knowledge of modern AI engineering concepts, including agent orchestration, interoperability frameworks, evaluation pipelines, vector-based storage, and AI observability.
- Experience defining AI platform strategies, including model selection, APIs, fine-tuning, and cost optimization.
- Strong understanding of security, governance, responsible AI practices, and compliance considerations in regulated environments.
- Excellent communication skills with the ability to explain technical tradeoffs to engineering and business leaders.
- Strong mentoring abilities and a track record of increasing team effectiveness.
Responsibilities
- Define and own the technical strategy for AI products, platforms, and agentic development workflows.
- Create architecture decision records, technical standards, and engineering guidelines that influence multiple teams.
- Establish clear boundaries between AI services and the broader product ecosystem while building reusable platform capabilities.
- Lead architectural decisions related to AI infrastructure, distributed systems, persistence strategies, evaluation frameworks, and observability.
- Define standards for AI feature validation, monitoring, release criteria, and responsible AI practices.
- Guide the adoption of agentic development practices and evolve engineering workflows.
- Make data-driven build-versus-buy recommendations, including vendor evaluations and cost models.
- Act as a technical advisor across engineering teams and mentor senior engineers.
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