Principal AI Engineer
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.
About the job
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.
Skills
AWS, Azure, Kubernetes, RAG, LangGraph, Crewai, Prompt Engineering, Pinecone, Weaviate, Pgvector, vLLM, Sglang, Terraform, Bicep, OpenTelemetry
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