Principal Engineer - Search

We are looking for a Principal Engineer (Search) to lead the vision, architecture, and evolution of Snapmint's Search platform. This role is responsible for building industry-leading search experiences that help millions of users discover relevant products quickly and efficiently across Snapmint's ecommerce ecosystem. As a principal engineer, you will drive technical strategy, mentor engineering teams, influence cross-functional product decisions, and architect highly scalable, low-latency search systems leveraging modern information retrieval, machine learning, and generative AI technologies.

Responsibilities:

• Define and drive the vision, architecture, and roadmap for Snapmint's Search and Discovery platform.
• Architect scalable, low-latency, and highly available search systems serving millions of requests.
• Lead search relevance initiatives across query understanding, ranking, semantic retrieval, personalization, and GenAI-powered search.
• Design and optimize indexing pipelines, search APIs, and core discovery capabilities, including autocomplete, recommendations, filters, and zero-result recovery.
• Establish best practices for scalability, performance, observability, and operational excellence.
• Define search quality metrics (CTR, conversion, recall, precision, and latency) and drive continuous improvement through experimentation and A/B testing.
• Partner with Product, Data Science, Catalogue, and Personalization teams to enhance product discoverability and customer experience.
• Mentor senior engineers and provide technical leadership across the organization.

Requirements:

• 12+ years of software engineering experience, with significant experience building large-scale search, discovery, recommendation, or information retrieval systems.
• Strong expertise in Elasticsearch, OpenSearch, Apache Solr, or similar search technologies.
• Deep understanding of information Retrieval fundamentals, including inverted indexes, tokenization, analysers, BM25 query parsing, faceting, relevance tuning, and ranking models.
• Proven experience designing and operating large-scale, distributed backend systems.
• Strong hands-on experience with query understanding systems such as spell correction, synonym management, multilingual search, transliteration, and query expansion.
• Experience building scalable search APIs and search platforms serving high-volume traffic.
• Expertise in search performance optimization, capacity planning, caching strategies, and low-latency architecture.
• Strong knowledge of semantic search, embeddings, vector databases, and hybrid retrieval architectures.
• Experience implementing learning-to-rank frameworks and ML-driven ranking systems in production environments.
• Demonstrated ability to lead large technical initiatives and influence cross-functional engineering organizations.

Good to Have:

• Experience building search and discovery platforms for e-commerce, fintech marketplaces, or large consumer internet products.
• Experience with generative AI and LLM-powered search, including conversational search, Retrieval-Augmented Generation (RAG), query rewriting, search assistants, and search personalization.
• Experience with vector databases and modern retrieval frameworks.
• Familiarity with recommendation systems and personalization engines.
• Experience leading organization-wide architecture reviews and technical strategy initiatives.
• Strong track record of mentoring senior engineers and growing technical leadership within teams.
• Experience driving experimentation frameworks and relevance optimization through A/B testing at scale.