Responsibilities:
Advocate a culture of data-driven improvement. Oversee system debugging, performance tuning, and technical debt management to ensure robust and reliable production environments.
Partner with the Technical Lead to define and drive the technical roadmap, while maintaining strong technical leadership and oversight for search and recommendation systems. Ensure the end-to-end reliability and quality of models from data ingestion and indexing to serving.
Drive the development of advanced Information Retrieval and Machine Learning systems, including embedding-based search, ranking algorithms, and natural language query understanding.
Define and implement rigorous evaluation frameworks and success metrics. Use offline/online evaluation, user feedback signals to continuously improve search relevance and user satisfaction.
Foster a high-performing and engineering culture. Provide active coaching, performance feedback, and personalized career development to empower engineers at all levels.
Minimum qualifications:
Bachelor’s degree or equivalent practical experience.
8 years of experience with managing and growing a team of software engineers, with experience in performance management and career development.
5 years of experience in a people management, supervision/team leadership role.
Experience with software engineering in Information Retrieval (IR), Natural Language Processing (NLP) or Machine Learning (ML).
Experience with designing, implementing, and optimizing large-scale, high-performance, distributed search or quality systems in production environments.
Preferred qualifications:
Master's degree or PhD in technology or equivalent practical experience in Computer Science, Machine Learning, Natural Language Processing, Information Retrieval, or a related technical field.
Experience with embedding-based retrieval (EBR), vector search, query understanding (qNLU), or large language models (LLMs) applied to search/recommendation systems.
Experience managing system latency improvements and scalability enhancements.
Good understanding of agentic architectures, tool-calling, and Retrieval-Augmented Generation (RAG) grounding layers.
Exceptional collaboration and communication skills, with a proven ability to align cross-functional stakeholders and navigate competing priorities.
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