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Solution Architecture for AIP Users: Partner directly with Data Scientists and ML engineers across
Large-Scale Training: Drive the state of large-scale training on AIP — throughput, reliability
Faster Iteration and Model Quality: Attack the end-to-end loop from idea to shipped model — data
Platform Integration: Design and drive integrations across AIP surfaces (model serving, ML
User Experience Translation: Convert pain points surfaced through embeddings, support channels, and
Enablement at Scale: Produce reference architectures, patterns, and opinionated best-practice

Advanced MLOps & ML Platform Engineering: Expert-level mastery of ML lifecycle platforms (e.g.
Distributed Systems & Infrastructure: At least 10 years of experience in Kubernetes
Architecture Design: Outstanding system design capability for available, scalable, and secure
AI/LLM System Experience: Hands-on experience with LLM orchestration, fine-tuning infrastructure
Innovation & AI Fluency: A learning mindset to evaluate and implement state-of-the-art (SOTA)
Adaptive Execution & Ownership: You can operate independently in high-ambiguity environments

Principal Machine Learning Engineer

Meet JobCopilot: Your Personal AI Job Hunter