Lead AI Data Trainer & AI Model Optimization Engineer

About this role

Everpure (NYSE: P) has evolved from storage pioneer to data platform, closing fiscal 2026 with $3.7 billion in revenue, its first billion-dollar quarter, and accelerating growth into FY27. Our strategic agenda spans the companies defining the next era of technology - hyperscalers, AI labs, the AI hardware supply chain, data platform providers, and the broader AI ecosystem. This type of work—work that changes the world—is what the tech industry was founded on.

So, if you're ready to seize the endless opportunities and leave your mark, come join us. THE ROLE We are seeking a Lead AI Data Trainer & AI Model Optimization Engineer to develop, guide, and continuously improve AI models that power intelligent document processing, entity extraction, document classification, and compliance automation solutions. In this role, you will lead the development of training pipelines, training datasets, and model optimization approaches across Small Language Models (SLMs), FastText classifiers, LLM-assisted workflows, and domain-specific extraction engines.

You will help define best practices, influence technical direction, and support AI solutions across regulated and data-intensive industries, including Financial Services, Healthcare, Real Estate, Insurance, and Government. This role combines data science, machine learning operations (MLOps), document intelligence, automation engineering, and quality assurance practices to deliver accurate, explainable, and scalable AI solutions in enterprise production environments.

WHAT YOU'LL DO Lead the development, training, evaluation, and optimization of AI models supporting document intelligence, entity extraction, document classification, information retrieval, and compliance automation use cases. Define and evolve training pipelines, datasets, evaluation frameworks, and automated workflows that support scalable model development and continuous improvement. Guide the optimization of SLMs, FastText classifiers, LLM-assisted workflows, and hybrid AI extraction solutions to meet customer, business, and operational requirements.

Establish domain intelligence frameworks and best practices that support regulated industries and compliance standards, including GDPR, PCI-DSS, HIPAA, SOC 2, and related requirements. Define model quality metrics, validation approaches, and performance standards to improve accuracy, explainability, and operational effectiveness. Analyze production behavior, investigate model performance challenges, and collaborate with Product, Engineering, and Customer Success teams to drive continuous improvement.