# Principal AI Engineer

**Company:** [PointClickCare](https://hotfix.jobs/companies/pointclickcare)
**Location:** Mississauga, Canada
**Role:** AI Research
**Experience:** 8+ years
**Skills:** AWS, Azure, Kubernetes, RAG, LangGraph, Crewai, Prompt Engineering, Pinecone, Weaviate, Pgvector, vLLM, Sglang, Terraform, Bicep, OpenTelemetry
**Posted:** 2026-08-20

> The Principal AI Engineer architects enterprise-grade AI/ML platforms and GenAI infrastructure, establishing standards for agentic systems, RAG, model serving, observability, security, and multi-cloud deployment. The role requires 8+ years in cloud architecture and platform engineering, including substantial AI/ML infrastructure experience.

## Job Description

## Responsibilities
- Design and document enterprise AI platform architectures, including reference implementations for agentic systems, RAG pipelines, and multimodal AI applications with guardrails, observability, and security patterns.
- Define architectural standards, design patterns, and best practices for GenAI infrastructure, including model serving, prompt management, vector storage, evaluation frameworks, and LLMOps pipelines.
- Lead technical evaluations and vendor assessments for AI infrastructure components, such as model gateways, vector databases, and observability tools.
- Collaborate with product, engineering, and platform teams to translate business requirements into scalable AI architectural solutions.
- Establish architectural governance for AI/ML workloads, including security controls, compliance frameworks, cost optimization strategies, and multi-cloud deployment patterns.

## Requirements
- 8+ years of experience in cloud architecture and platform engineering with AWS and/or Azure, including at least 3+ years focused on AI/ML infrastructure and GenAI solutions.
- Proven experience designing and implementing enterprise-scale AI/ML platforms supporting multiple product teams and use cases.
- Deep expertise in cloud-native architectures, including microservices, event-driven systems, serverless patterns, and Kubernetes.
- Strong understanding of GenAI architectural patterns, including RAG, agentic frameworks, prompt engineering, and LLM evaluation methodologies.
- Experience with AI infrastructure components such as vector databases, model serving platforms, and prompt management systems.

## Nice-to-haves
- Experience with Azure OpenAI Service, Azure AI Studio, and Azure Machine Learning in healthcare or regulated industries.
- Familiarity with AI observability frameworks and production monitoring strategies.
- Understanding of healthcare compliance requirements, including HIPAA and PHIPA, and security frameworks for AI applications.
- Experience with Infrastructure as Code using Terraform or Bicep and GitOps practices for AI platform automation.

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