Principal AI Engineer
Location
- Vancouver, Canada
About the Role
We’re seeking a hands-on AI expert to lead the development of intelligent agents that operate as microservices within a hybrid edge cloud platform. Your responsibilities include designing, training, and optimizing AI models for distributed execution—from smart devices to servers—while innovating new agent capabilities and collaborating with platform and product teams.
You will work at the nexus of AI, microservices, and edge computing to bring contextual, autonomous intelligence across the entire compute continuum.
Key Responsibilities
- Design and implement AI agents for contextual tasks across end devices, cloud, and hybrid environments.
- Train and fine-tune models including LLMs, SLMs, and multimodal models to support agent behaviors.
- Architect modular, lightweight AI components for local execution as microservices.
- Collaborate with product and platform teams to define capabilities and deployment constraints.
- Prototype AI Chains and other orchestration patterns for chained or cooperative agents.
- Build APIs and interfaces for agent integration with applications and devices.
- Lead a team of engineers in deploying, testing, and developing agent workflows across various devices.
- Stay updated on advancements in generative AI, reinforcement learning, autonomous agents, and on-device inference techniques.
Required Qualifications
- 5+ years experience in AI/ML development with real-world product deployment.
- Experience 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 techniques.
- Proficiency in Python and libraries such as PyTorch, TensorFlow, HuggingFace Transformers, LangChain, or similar.
- Familiarity with Docker, REST APIs, gRPC, and orchestration tools like Kubernetes.
- Solid understanding of AI system design, agent architectures, and contextual AI.
- Ability to mentor junior team members and contribute to team growth.
Nice to Have
- Experience with frameworks like ReAct, AutoGPT, BabyAGI.
- Knowledge of federated learning, on-device training, and personalized edge inference.
- Experience with real-time data processing and offline-first applications.
- Prior work with hybrid edge platforms and running AI on IoT devices, smartphones, or industrial PCs.
Additional Notes
Keep in mind, the job emphasizes leadership in AI microservices, edge computing, and autonomous agent development.