Principal AI Engineer

About the role

The Principal AI Engineer will design and architect scalable, secure, and enterprise‑grade AI/ML platforms that enable product teams to rapidly build and deploy AI applications. Working at the intersection of product strategy and technical implementation, this role defines architectural patterns, establishes technical standards, and guides engineering teams in building cloud‑native AI infrastructure aligned with organizational goals.

Key responsibilities

• Design and document enterprise AI platform architectures, including reference implementations for agentic systems, RAG pipelines, and multi‑modal AI applications with integrated guardrails, observability, and security patterns.
• Define and maintain architectural standards, design patterns, and best practices for GenAI infrastructure such as model serving, prompt management, vector storage, evaluation frameworks, and LLMOps pipelines.
• Lead technical evaluations and vendor assessments for AI infrastructure components (model gateways, vector databases, observability tools) and provide architectural recommendations.
• Collaborate with product, engineering, and platform teams to translate business requirements into scalable AI architectural solutions, ensuring consistency across multiple product implementations.
• Establish architectural governance for AI/ML workloads, including security controls, compliance frameworks, cost‑optimization strategies, and multi‑cloud deployment patterns (Azure, AWS).

Required profile

• 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 track record designing and implementing enterprise‑scale AI/ML platforms supporting multiple product teams and use cases.
• Deep expertise in cloud‑native architectures (microservices, event‑driven systems, serverless patterns, Kubernetes).
• Strong understanding of GenAI architectural patterns such as RAG, agentic frameworks (LangGraph, CrewAI), prompt engineering, and LLM evaluation methodologies.
• Experience with AI infrastructure components like vector databases (Pinecone, Weaviate, pgvector) and model serving platforms (vLLM, SGLang, Azure AI).

Required skills

• Azure OpenAI Service, Azure AI Studio, Azure Machine Learning
• OpenTelemetry, MLFlow, Arize, LangSmith
• Terraform, Bicep, GitOps
• AWS, Azure, Kubernetes
• LangGraph, CrewAI
• Pinecone, Weaviate, pgvector
• vLLM, SGLang, Azure AI model serving