Principal AI Engineer, Distributed Systems & Intelligent Platforms
Confirmed on the employer's own hiring board on Oct 23, 2026. First seen by Alion on Jul 30, 2026. Calliere scores B on the Alion truth index.
This is a remote position.
Principal AI Engineer
Distributed Systems & Intelligent Platforms
Remote|Canada|Full-Time
ABOUT OUR CLIENT
Our client is an innovation studio that turns complex business challenges into elegant, scalable software. They ship products that matter, and obsess over the craft of building them well. As one of only eleven organizations globally to hold a premium-tier cloud architecture partnership, they operate at a level most teams never reach.
THE OPPORTUNITY
Our client isn't hiring for a blank-slate prototype. They need someone who gets energized by walking into a live, high-traffic system with real stakes, and making it better. You'll be a cornerstone contributor on a focused, cross-functional pod, driving full-stack delivery on an AI-native learning platform that's reimagining how people grow and develop skills in an era of intelligent machines.
This is a role for someone who loves solving genuinely hard problems: distributed workflows, complex integrations, agentic systems, and production reliability at scale.
WHAT YOU'LL DO
Engineering & Delivery
Build and own backend services in Python and Node.js, architect serverless compute layers, and develop the APIs that tie frontend experiences to the underlying data and logic. You write code you're proud to have reviewed.
Orchestration & Event-Driven Architecture
Contribute to sophisticated workflow engines; state machines, asynchronous event pipelines, and reliable retry/failure patterns across distributed services.
API & Data Layer
Design and evolve RESTful interfaces, integrate third-party and internal systems, and leverage advanced NoSQL data modeling techniques (composite key strategies, transactional operations) to keep things fast and correct under load.
Production Ownership
When things break, and in production, things break, you're the kind of engineer who wants to be in the room. Monitor, debug, and continuously improve system observability and deployment reliability on cloud-native infrastructure.
Team & Culture
Collaborate across time zones, contribute meaningfully in code reviews, take end-to-end ownership of features, and help shape how the team works, not just what it ships.
Requirements
- At least 6 years of experience building production software professionally
- Deep hands-on experience with Python and Node.js
- AWS production experience: Lambda, DynamoDB, S3, SQS, EventBridge, Step Functions
- Advanced NoSQL data modeling; composite key design, transactional writes, scalable single-table patterns
- LLM engineering experience: tool/function calling, prompt construction, multi-agent coordination, vector search, embedding pipelines, retrieval-augmented generation (RAG)
- Full-stack development capability with frontend integration experience
- Strong unit testing discipline and maintainable test strategies
- Container-based deployment experience (Docker)
- Solid RESTful API design fundamentals
NICE TO HAVE
- Ability to reason about system-wide impact before writing a line of code, including downstream effects across services and data layers
- Track record of catching requirement gaps and design holes before they reach development
- Hands-on experience with Langfuse, synthetic AI pipeline testing, or token cost optimization strategies
- Step Functions applied specifically to AI workflow orchestration
- Reasoning effort configuration for LLM-based systems
If you've been looking for a role where your AI engineering skills meet genuine product complexity, and where the infrastructure is actually interesting, this is it.