Technical lead building safe, evaluable LLM agents and AI infrastructure for OpenLoop’s telehealth platform. Setting architecture, observability, retrieval, and model operations direction.
About the role
- Principal AI Engineer architecting secure, scalable AI/ML platforms for PointClickCare’s healthcare technology. Establishing GenAI standards and enabling product teams to deploy AI applications.
Responsibilities
- Design and document enterprise AI platform architectures, including reference implementations for agentic systems, RAG pipelines, and multi-modal AI applications
- Integrate guardrails, observability, and security patterns into AI applications
- Define and maintain architectural standards, design patterns, and best practices for GenAI infrastructure
- Lead technical evaluations and vendor assessments for AI infrastructure components
- Provide architectural recommendations aligned with organizational requirements
- 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
- Evaluate emerging AI technologies and align platform capabilities with product roadmap requirements
- Guide engineering teams in building cloud-native AI infrastructure
- Develop scalable platform capabilities, playbooks, and patterns that accelerate AI implementation across the organization
Requirements
- 8+ years of experience in cloud architecture and platform engineering with AWS and/or Azure
- At least 3+ years focused on AI/ML infrastructure and GenAI solutions
- Proven track record 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 container orchestration (Kubernetes)
- Strong understanding of GenAI architectural patterns including RAG, agentic frameworks (LangGraph, CrewAI), prompt engineering, and LLM evaluation methodologies
- Experience with AI infrastructure components such as vector databases (Pinecone, Weaviate, pgvector), model serving platforms (vLLM, SGLang, Azure AI), and prompt management systems
- Experience with Azure OpenAI Service, Azure AI Studio, and Azure Machine Learning platforms in healthcare or regulated industries
- Familiarity with observability frameworks for AI systems (OpenTelemetry, MLFlow, Arize, LangSmith) and production monitoring strategies
- Understanding of healthcare compliance requirements (HIPAA, PHIPA) and security frameworks for AI applications
- Experience with Infrastructure as Code (Terraform, Bicep) and GitOps practices for AI platform automation
Benefits
- Benefits starting from Day 1
- Retirement Plan Matching
- Flexible Paid Time Off
- Wellness Support Programs and Resources
- Parental & Caregiver Leaves
- Fertility & Adoption Support
- Continuous Development Support Program
- Employee Assistance Program
- Allyship and Inclusion Communities
- Employee Recognition
- Bonus
- Flexible work arrangements
- In-office events and team meetings for hybrid roles
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