ML System Architecture and Design : Architect, design, and oversee the implementation of ML systems with a strong focus on data pipelines, training/inferencing MLOps, and service/model performance tracking.
AI Research : Stay updated with the latest advancements in AI, particularly generative AI; identify suitable technologies and models for Splore use cases, apply rigorous experimentation practices, and drive continuous ML feature upgrades.
ML Evaluation and Improvement : Establish a consistent approach for model evaluation with an emphasis on business impact, enforce appropriate model training and evaluation practices with relevant metrics to justify business value.
POC Management and Facilitation : Collaborate closely with Product Management and Sales to design and develop POC AI solutions, demonstrating and justifying their impact.
ML Operations and Scaling : Assess business needs to design and implement suitable ML training and inferencing pipelines and frameworks, promote best practices for ML engineering in training and model optimization, and design mechanisms for evaluating and tracking model performance.
Technical Leadership and Oversight : Provide technical guidance to AI engineers, ensuring high-quality technical delivery and continuous growth. Offer career advice and growth opportunities for engineers at various seniority levels.
