Information Retrieval Expert

Job Purpose

We are looking for highly skilled Search Systems Engineer to architect, scale, and optimize our core search infrastructure. You will handle massive, complex datasets across diverse sources, building robust indexing pipelines and querying systems that power our core platform. Your primary focus will be on deep search engine optimization, distributed systems architecture, and advanced relevance tuning to deliver lightning-fast, highly accurate search experiences for our users.

Qualifications and Skills

Education

• Bachelors in Engineering, or a Masters in Computer Science, Mathematics, or a related technical discipline.

Mandatory Skills

• 4+ years of robust experience in Software Engineering, specifically focused on Search Infrastructure, Information Retrieval, or Distributed Systems.
• Deep, hands‑on administrative and developmental experience with enterprise search engines (Elasticsearch, Apache Solr, OpenSearch).
• Experience with high‑performance, real‑time search and ranking engines like Elastic search and Vespa.
• Expert‑level coding skills in Java, Python or C#, with a strong grasp of OOP, design patterns, and concurrency.
• Deep understanding of Information Retrieval fundamentals.
• Extensive experience building RESTful web services and microservices; solid understanding of HTTP, JSON, and network protocols.

Good to Have Skills

• Experience handling massive datasets using streaming and processing platforms (e.g., Apache Kafka, Spark, Flink).
• Strong skills in Docker and Kubernetes for containerizing and orchestrating highly available search infrastructure.
• Experience with Machine Learning specifically applied to search (e.g., Learning to Rank, click models, A/B testing search algorithms).
• Experience with system monitoring, profiling, and benchmarking tools (e.g., Prometheus, Grafana, Datadog) to ensure search cluster stability.

Responsibilities

• Design, build, and maintain highly available, distributed search architectures capable of handling massive scale and high throughput.
• Deploy, configure, and optimize modern search engines including Elasticsearch, OpenSearch, Apache Solr, and Vespa, selecting the right tool for specific workload requirements.
• Tune search relevance, build custom scoring functions, and implement advanced ranking models (such as Learning to Rank) to continuously improve search accuracy and the overall customer experience.
• Deeply analyze and optimize query performance, latency, indexing speed, and cluster health; manage sharding, replication, and memory allocation strategies.
• Develop scalable, fault‑tolerant data ingestion pipelines to reliably extract, transform, and load (ETL) complex data into search indices in real‑time or near‑real‑time.
• Implement complex search features including faceted search, autosuggest, spell correction, synonyms management, and geospatial querying.