Principal AI Engineer, Distributed Systems & Intelligent Platforms in Canada Creek, Nova Scotia at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal AI Engineer, Distributed Systems & Intelligent Platforms based in Canada.

This is a senior technical role focused on building and scaling intelligent, AI-native platforms operating at production-grade complexity. You will work on live, high-traffic distributed systems where reliability, performance, and architectural clarity are critical. The role blends AI engineering with distributed systems design, requiring deep expertise in backend development, cloud infrastructure, and event-driven architectures. You will contribute to building intelligent workflows, agentic systems, and scalable APIs that power next-generation learning and development platforms. The environment is highly collaborative, engineering-driven, and focused on solving complex real-world problems at scale. It is ideal for a hands-on principal-level engineer who thrives in deep technical ownership and production-critical systems.

In this role, you will be responsible for designing, building, and scaling distributed AI-driven systems and backend platforms that support high-traffic, production-grade applications.

• Design, build, and maintain backend services using Python and Node.js within cloud-native architectures
• Develop and evolve APIs and integration layers connecting frontend applications with distributed backend systems
• Architect and implement event-driven systems, including workflows, state machines, and asynchronous processing pipelines
• Contribute to advanced NoSQL data modeling, including scalable schema design, transactional patterns, and performance optimization
• Build and support AI-driven systems including LLM workflows, RAG pipelines, embeddings, and multi-agent orchestration
• Own production systems, including monitoring, debugging, observability, and continuous reliability improvements
• Collaborate with cross-functional engineering teams to deliver end-to-end features and improve system architecture
• Participate in code reviews, technical design discussions, and contribute to engineering best practices across the team

The ideal candidate brings deep experience in distributed systems, backend engineering, and production AI systems, with strong ownership of scalable architecture.

• 6+ years of professional software engineering experience building production-grade systems
• Strong expertise in Python and Node.js development
• Hands-on experience with AWS services such as Lambda, DynamoDB, S3, SQS, EventBridge, and Step Functions
• Strong understanding of event-driven architecture and distributed system design principles
• Advanced NoSQL data modeling experience, including composite keys and scalable single-table designs
• Practical experience with LLM-based systems, including RAG, embeddings, prompt engineering, and tool/function calling
• Experience designing and integrating RESTful APIs and backend services at scale
• Strong unit testing practices and commitment to maintainable, high-quality codebases
• Experience with containerization (Docker) and cloud deployment workflows

Nice to have:

• Experience reasoning about system-wide impact and cross-service dependencies
• Familiarity with AI observability tools such as Langfuse or similar platforms
• Experience optimizing AI systems for cost, latency, and performance
• Exposure to AI workflow orchestration using Step Functions or similar tools
• Experience in synthetic testing or evaluation of AI pipelines
• Strong ability to identify architectural gaps early in the design process

• Competitive compensation aligned with principal-level engineering roles
• Fully remote-first working model across Canada
• Opportunity to work on advanced AI-native and distributed systems at production scale
• High technical ownership with significant architectural influence
• Collaborative, engineering-led environment focused on deep technical excellence
• Exposure to cutting-edge LLM systems, agentic architectures, and cloud-native infrastructure
• Strong focus on learning, experimentation, and continuous improvement
• Opportunity to shape core platform architecture and long-term technical direction