Search Systems (Lucene, Elasticsearch, Solr,) Software Engineering Lead

Elsevier Inc. is a global leader in advanced information and decision support for science and healthcare. They are seeking a Search Systems Software Engineering Lead to provide hands-on technical leadership for the Search Experience team, guiding the design and delivery of scalable search and retrieval systems while leading a group of engineers.

Responsibilities:

• Providing technical leadership for the Search Experience team, guiding design and implementation of shared search and retrieval systems
• Owning the technical delivery of search platform initiatives, ensuring solutions meet requirements for scalability, relevance, reliability, and maintainability
• Leading our shared search platform — expanding content search and improving relevance through vector and lexical search techniques
• Designing and developing scalable search services, data processing workflows, and microservices using technologies such as Elasticsearch, Spark, and Airflow
• Writing clean, modular, and testable code in Python and/or Java, aligned with architecture guidelines and engineering standards
• Leading design discussions, code reviews, and architecture sessions to ensure software quality and maintainability
• Mentoring and supporting engineers through pairing, code reviews, and technical coaching
• Proactively identifying technical risks, dependencies, and bottlenecks, and drive them to resolution
• Contributing to cross-team alignment, ensuring the search platform integrates cleanly with broader product and AI ecosystems

Requirements:

• Current expertise with Lucene, Elasticsearch, Solr, or similar search engines, with industry experience in semantic and lexical search. Only candidates with Search Technology will be considered for this role
• Demonstrated experience acting as a technical lead on complex backend or search platform systems
• Proven track record building and scaling search systems in production environments
• Current and extensive development skills in Python and/or Java; Scala is a plus
• Solid backend engineering fundamentals: API design, data modelling, distributed systems, and performance tuning
• Proven ability to balance hands-on development with technical leadership and cross-functional coordination
• Experience with Agile or Kanban teams, collaborating across functions
• Experience building or integrating AI/LLM-powered or GenAI applications
• Familiarity with vector/embedding-based search and KNN algorithms
• Exposure to graph-based data models or knowledge graph architecture
• Experience working on internal developer platforms or shared infrastructure used by multiple teams
• Knowledge of observability best practices for distributed data systems (e.g., metrics, logs, alerts)