Senior Principal Engineer – AI Search & Retrieval

• Bachelor’s degree in Computer Science or a related field, or equivalent practical work experience.
• 15+ years of experience designing complex software systems, with significant hands-on engineering experience in languages such as Java and cloud platforms such as AWS.
• 5+ years of experience in a technical leadership role within a large and distributed organization, designing systems operating at significant scale.
• Strong experience with Information Retrieval and search systems , including technologies such as Lucene, Elasticsearch or OpenSearch.
• Experience designing or building AI/ML-powered search, recommendation, retrieval or ranking systems .
• Strong understanding of Large Language Models (LLMs) and their application to search, including query understanding, query rewriting/expansion, retrieval augmentation, ranking/reranking or related search use cases.
• Understanding of modern ranking and reranking approaches , including ML-based ranking, neural rerankers and/or LLM-based reranking.
• Experience with embeddings, vector search/vector databases and semantic or hybrid retrieval architectures .
• Understanding of the lifecycle of ML/LLM-powered production systems, including model integration, inference, evaluation, observability, latency and cost considerations.
• Expertise in cloud infrastructure platforms such as AWS.
• Thorough understanding of distributed systems, including scalability, performance, reliability, security and consistency characteristics.
• Experience building multi-tenant SaaS architectures, with a deep appreciation for platform and API-first design.
• Expertise with modern technology stacks, API design, microservices, stream processing, resilience and operational excellence.
• Experience monitoring and improving the reliability of complex systems at scale.
• Exposure to managing security and compliance challenges in cloud products and/or platforms.

• Experience building query orchestration or query intelligence systems that use multiple retrieval strategies, models or data sources.
• Experience with learning-to-rank, neural ranking, cross-encoders, rerankers or LLM-based relevance models .
• Experience designing RAG or retrieval-augmented AI architectures , particularly where retrieval quality and grounding are critical.
• Experience with search relevance evaluation, including offline evaluation, human relevance judgments, experimentation/A/B testing and production quality metrics.
• Understanding of the trade-offs between lexical retrieval, embedding-based retrieval, neural reranking and LLM-based approaches.
• Experience optimizing AI/search architectures for latency, quality, throughput and inference cost at production scale.
• Familiarity with modern ML infrastructure and model serving architectures.

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