About Cornerstone:
Cornerstone powers the future-ready workforce with modern, AI-driven employment solutions. Our platform enables companies to develop, manage, and engage their talent—unlocking growth and innovation across organizations of all sizes.
Who We’re Looking For:
Cornerstone is seeking a visionary and highly accomplished Distinguished Principal AI Engineer to spearhead the creation of groundbreaking AI and machine learning solutions across our industry-leading workforce agility - Galaxy ecosystem. This is a rare opportunity for a true innovator—someone who thrives on architecting and hands-on building intelligent, autonomous systems at massive scale. You’ll be driving the future of workforce technology by delivering AI-powered applications that are robust, secure, ethical, and brilliantly performant.
In this role you will…
- Advanced Architecture & System Design: Architect, implement, and scale modern, distributed AI systems—including training pipelines, streaming data processing, serverless microservices, and MLOps infrastructure—to deliver enterprise-grade reliability and security.
- Lead end-to-end technical delivery of AI Native Products —owning E2E architecture/design and hands-on engineering excellence from design through deployment, scaling, and continuous improvement.
- Lead E2E architecture for AI native SaaS Products —distributed systems, APIs, integrations, security, and reliability.
- Drive hands-on software engineering across the full lifecycle (design, build, deploy, optimize) with strong engineering fundamentals.
- Architect and scale modern distributed architectures (serverless/microservices, Kubernetes, Docker) and data/compute workflows.
- Build production-grade AI/ML capabilities (LLMs/GenAI, NLP, recommenders) with MLOps-grade reliability and monitoring.
- Design end-to-end streaming + batch data pipelines (ETL, Spark, data lakes) and scalable inference patterns- Deliver agentic/LLM workflows (orchestration, tools, vector search, knowledge graphs) with robust integration to enterprise systems.
- Set standards for AI safety, fairness, explainability, privacy, and compliance.
- Own CI/CD, DevSecOps, and GitOps practices to ensure secure, repeatable, enterprise deployments.
- Mentor teams through architecture reviews, best practices, and engineering excellence—driving responsible engineering across SDLC.
You’ve got what it takes if you have…
- 12+ years of software engineering with proven ownership in product engineering, architecture & operational excellence.
- Deep expertise in engineering fundamentals (system design, distributed systems, data/compute architecture, scalability, security).
- Strong hands-on experience leading complex architecture and delivery for enterprise platforms.
- Proven experience with cloud (AWS/Azure/GCP), Kubernetes/Docker, and modern API/service design.
- Strong background in ML/LLM/GenAI systems delivery or architecting AI-enabled solutions with production-grade MLOps practices.
- Experience with CI/CD, GitOps, DevSecOps, and enterprise security practices- Strong understanding of responsible AI (privacy, bias, explainability) and real-world deployment constraints.
- HR tech/SaaS/enterprise domain experience is a plus.
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