Principal AI Engineer

cyberu•Dublin, CA

2d•Onsite

About The Position

We are seeking a Principal AI Engineer to design, build, and scale AI-driven systems across our product platforms. This role is ideal for a deeply technical engineer who leads through hands-on contribution, solves complex problems, and drives the adoption of AI into real-world products. The ideal candidate brings strong backend and cloud fundamentals, mandatory experience with Java and AWS, and proven experience integrating AI and Generative AI capabilities into production systems. You will work closely with product, architecture, and engineering teams to deliver reliable, scalable, AI-enabled solutions that create meaningful business impact. Cornerstone powers the potential of organizations and their people to thrive in a changing world. Cornerstone Galaxy, the complete AI-powered workforce agility platform, meets organizations where they are. With Galaxy, organizations can identify skills gaps and development opportunities, retain and engage top talent, and provide multimodal learning experiences to meet the diverse needs of the modern workforce. More than 7,000 organizations and 100 million+ users in 180+ countries and in nearly 50 languages use Cornerstone Galaxy to build high-performing, future-ready organizations and people today.

Requirements

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

Nice To Haves

Experience with Generative AI platforms and frameworks (AWS Bedrock, OpenAI APIs, LangChain, LlamaIndex).
Experience with vector databases and semantic search.
Familiarity with MLOps practices, model monitoring, and evaluation frameworks.
Experience working in product-driven, agile environments.

Responsibilities

Design, build, and operate AI-powered applications, including LLM-based services, ML-driven workflows, and intelligent automation.
Integrate AI capabilities into Java-based backend systems and cloud-native platforms.
Design scalable API-first services (REST & GraphQL) exposing AI capabilities across products.
Implement data pipelines supporting feature engineering, retrieval-augmented generation (RAG), vector search, and inference.
Apply best practices for prompt design, model orchestration, evaluation, and observability.
Build and operate cloud-native AI systems on AWS, including EC2, S3, RDS, Lambda, and EKS.
Design containerized workloads using Docker and Kubernetes (EKS).
Optimize systems for scalability, latency, reliability, and cost efficiency.
Implement Infrastructure as Code using Terraform or AWS CDK.
Design and operate relational (SQL) and NoSQL data stores for high-scale, distributed systems.
Make informed trade-offs around data consistency, availability, and performance.
Work with event-driven and asynchronous architectures where appropriate.
Lead complex technical initiatives end-to-end, from design through production deployment.
Influence system design and engineering standards across teams through design reviews and technical guidance.
Mentor senior engineers and raise the overall bar for AI and cloud engineering excellence.
Collaborate with Product, Data, UX, Security, and DevOps teams to deliver AI-driven product capabilities.