Principal AI Engineer

Location: Vancouver, Canada

About the Role:

We're looking for a hands-on AI expert to lead the development of intelligent agents

that run as microservices across the hybrid edge cloud platform. You will be

responsible for designing, training, and optimizing AI models tailored for distributed

execution, from smart devices to servers, while also building new agent capabilities

and coordinating with platform and product teams.

You'll work at the intersection of AI, microservices, and edge computing to bring

contextual, autonomous intelligence to the entire compute continuum.

Key Responsibilities:

 Design and implement AI agents that perform contextual tasks in end devices,

cloud, and hybrid environments.

 Train and fine-tune LLMs, SLMs, and multimodal models as needed to support

specific agent behavior.

 Architect modular and lightweight AI components that can run locally as

microservices.

 Collaborate with product and platform teams to define agent capabilities and

deployment constraints.

 Define and prototype AI Chains or other orchestration patterns for chained or

cooperative agents.

 Build APIs and interfaces for integrating agents with upstream applications and

downstream sensors/devices.

 Lead a small team of engineers building, deploying, and testing agentic

workflows across devices.

 Stay ahead of the curve on advances in generative AI, reinforcement learning,

autonomous agents, and on-device inference techniques.

Required Qualifications:

 5+ years of hands-on experience in AI/ML development, with focus on real-

world product deployment.

 Experience building and deploying AI models as microservices using

frameworks like TorchServe, TensorFlow Serving, ONNX, Triton, etc.

 Strong knowledge of LLMs, language agents, vision models, and lightweight

model optimization (e.g., quantization, pruning, distillation).

 Proficiency in Python, with experience in libraries such as PyTorch, TensorFlow,

HuggingFace Transformers, LangChain, or similar.

 Familiarity with Docker, REST APIs, gRPC, and working knowledge of

Kubernetes or edge orchestration tools.

 Strong understanding of contextual AI, agent architectures, or AI systems

design.

 Ability to mentor junior team members and contribute to team growth and

technical standards.

Nice to Have:

 Experience with ReAct, AutoGPT, BabyAGI, or other agentic frameworks.

 Understanding of federated learning, on-device training, or personalized edge

inference.

 Exposure to real-time data processing and offline-first applications.

 Prior experience with hybrid edge platforms or running AI on IoT devices,

smartphones, or industrial PCs.