What you'll do:

• Search Optimization: Design, develop, and optimize search algorithms using ElasticSearch, focusing on relevance, performance, and multi-language support.
• Recommendation Systems: Develop and enhance recommendation algorithms using collaborative filtering, content-based filtering, and hybrid approaches. Implement real-time, personalized product recommendations that drive engagement and sales.
• Scalable Architecture: Design scalable search and recommendation systems capable of handling millions of queries daily with a focus on high availability, low-latency, and resiliency.
• Data-Driven Decisions: Work closely with data science / analytics teams to leverage machine learning models, vector databases, and user behavior analytics to improve product ranking, personalization, and search relevance.
• Cloud Infrastructure: Implement and optimize search and recommendation infrastructure on AWS, GCP, or other cloud platforms. Ensure the system scales to meet traffic demands, optimize performance, and maintain high uptime.
• Multilingual Search: Lead efforts to enhance search functionality across multiple languages and regions, ensuring accurate, localized, and context-aware results.
• Innovation: Stay updated with the latest advancements in ElasticSearch, LLMs (Large Language Models), vector search, and recommendation algorithms. Apply these innovations to continuously improve the platform.
• Mentorship: Provide technical leadership and mentorship to junior engineers and collaborate with peers to promote a culture of engineering excellence.

What makes you a great fit:

• 8+ years of experience in software engineering, with a focus on search technologies, recommendation systems, or personalization.
• Proven expertise in ElasticSearch, including tuning relevance models, query performance optimization, and large-scale search infrastructure.
• Experience in Recommendations: Hands-on experience with recommendation algorithms such as collaborative filtering, content-based filtering, and hybrid models.
• Machine Learning Knowledge: Proficiency in machine learning techniques for search relevance, personalization, and user behavior modeling.
• Experience with Vector Search: Experience with vector-based databases (e.g., Pinecone, Milvus) for improving search through semantic understanding and natural language processing (NLP).
• Cloud Infrastructure Expertise: Solid understanding of cloud-based architectures on AWS, GCP, or other cloud platforms with experience in building and scaling distributed systems.
• Multilingual Search Experience: Proven ability to handle and optimize multilingual search results, including localization and handling cross-language queries.
• Strong programming skills in Python, Java, or Scala for building high-performance, real-time systems.
• Experience in Personalization: Experience integrating machine learning-driven personalization systems that improve user engagement.
• Excellent problem-solving skills and ability to deliver high-quality code in a fast-paced environment.
• Strong communication skills and ability to collaborate with cross-functional teams.
• Experience with LLM-based search systems (GPT-based models or similar) and integrating semantic search features.
  
• Knowledge of A/B testing frameworks to validate the impact of changes in search relevance and recommendation algorithms.
• Experience with stream processing technologies like Apache Kafka or Apache Flink for real-time data integration into search and recommendation engines.
• Familiarity with data pipeline tools such as Airflow or Spark for handling large-scale data processing and training recommendation models.