Principal AI Engineer - United States

Role Overview

Principal AI Engineer (Remote, United States)

You will design, build, and scale AI-driven systems across Cornerstone OnDemand’s product platforms. This is a hands-on leadership role where you solve complex problems, own systems end-to-end, and drive the adoption of AI into production products.

Responsibilities

• AI Engineering & System Design
  • Build and operate LLM-based services, ML-driven workflows, and intelligent automation
  • Integrate AI capabilities into Java backend systems and cloud-native platforms
  • Design API-first AI services exposing capabilities via REST and GraphQL
  • Implement data pipelines for feature engineering, RAG, vector search, and inference
  • Apply best practices for prompt design, model orchestration, evaluation, and observability
• Cloud & Platform Engineering (AWS – Must Have)
  • Operate AWS-native AI systems using EC2, S3, RDS, Lambda, and EKS
  • Containerize workloads with Docker and Kubernetes (EKS)
  • Optimize for scalability, latency, reliability, and cost
  • Use Infrastructure as Code with Terraform or AWS CDK
• Data & Distributed Systems
  • Operate SQL and NoSQL stores for high-scale distributed systems
  • Make trade-offs for consistency, availability, and performance
  • Work with event-driven/asynchronous architectures
• Technical Leadership & Impact
  • Lead complex initiatives end-to-end (design → production deployment)
  • Influence architecture and engineering standards through design reviews and technical guidance
  • Mentor senior engineers and raise the bar for AI and cloud excellence
  • Collaborate with Product, Data, UX, Security, and DevOps to deliver AI-enabled capabilities

Requirements

• 8–10 years of hands-on software engineering experience building large-scale production systems
• Strong hands-on Java experience for backend and distributed systems
• Mandatory AWS experience designing and operating cloud-native workloads
• Strong production experience with SQL and NoSQL databases
• 3+ years building AI-powered applications (ML/LLM systems)
• Experience with API-first architectures (REST, GraphQL)
• Hands-on experience with Docker and Kubernetes (EKS)
• Strong end-to-end ownership and problem-solving

Nice to Have

• Generative AI platforms/frameworks: AWS Bedrock, OpenAI APIs, LangChain, LlamaIndex
• Vector databases and semantic search
• MLOps: model monitoring and evaluation frameworks
• Experience in product-driven Agile environments

Leveling Guidance

• Senior AI Engineer: Owns complex components/services
• Principal AI Engineer: Owns multi-team technical initiatives; influences standards across the organization