Principal Data Engineer, LLM/AI Platforms

What you'll do

• Architect and optimize data platforms and pipelines for LLMs, RAG, and agentic systems at exabyte scale.
• Implement agentic workflows and harnessing techniques to create autonomous, data-driven security features.
• Develop high-quality, production-ready code focused on performance, maintainability, and rigorous testing.
• Establish MLOps and DataOps best practices for monitoring and automated recovery of AI services.
• Provide technical leadership in data modeling, normalization, and semantic cataloging for ML workloads.
• Mentor engineers through technical workshops and design reviews to build team expertise in AI technologies.
• Transform research prototypes into robust, production-grade services by collaborating with cross-functional teams.
• Manage the end-to-end lifecycle of critical data services including development, testing, deployment, and monitoring.

What we're looking for

• Master’s degree or PhD in Computer Science, Data Engineering, or a related STEM field, or equivalent practical experience.
• 10+ years of progressive experience in Data Engineering or Platform Engineering.
• At least 3 years of experience architecting and building platforms for AI/ML or Data Science at massive scale.
• Hands-on experience in LLM engineering, including fine-tuning, prompt engineering, deployment, RAG, and agentic workflows.
• Proven track record of designing and delivering large-scale distributed systems involving sharding, partitioning, and concurrency.
• Expert-level proficiency in a high-level coding language such as Python or JVM technologies.
• Proficiency with distributed data processing frameworks (Spark, Dask, Flink) and cloud platforms (AWS, GCP, or OCI).
• Experience in a Principal or Staff level engineering role demonstrating technical leadership and mentorship capabilities.