We are looking for a highly skilled Senior Backend Engineer (Search) to design, build, and optimise large-scale search and discovery systems for e-commerce platforms. The ideal candidate should have strong experience in backend engineering, distributed systems, search technologies, and relevance optimisation, with expertise in building low-latency, high-throughput services. You will work closely with data scientists, ML engineers, product managers, and frontend teams to improve search relevance, ranking quality, query understanding, and overall customer discovery experience.
Responsibilities
- Design and develop scalable backend services powering search, ranking, recommendation, and discovery systems.
- Build and optimise APIs and microservices for high-performance search experiences.
- Develop robust pipelines for ingestion, indexing, and real-time search updates.
- Collaborate with ML and data science teams to productionise ranking and recommendation models.
- Optimise latency, throughput, and reliability of search infrastructure and distributed systems.
- Implement monitoring, observability, alerting, and debugging mechanisms for search systems.
- Drive engineering best practices, including code quality, testing, CI/CD, documentation, and system design reviews.
- Mentor junior engineers and contribute to architectural decisions across the platform.
Requirements
- 5+ years of experience in backend software engineering.
- Strong proficiency in one or more backend languages such as Java, Kotlin, Python, Go, or Scala.
- Strong understanding of distributed systems, microservices architecture, and scalable backend design.
- Experience designing REST/gRPC APIs and asynchronous event-driven systems.
- Solid understanding of search concepts, including indexing, ranking, relevance, tokenisation, faceting, and query optimisation.
- Experience working with cloud platforms such as AWS, GCP, or Azure.
- Familiarity with Kafka, Pub/Sub, Redis, caching strategies, and real-time processing systems.
- Strong SQL and data modelling skills.
- Experience with observability tools, logging, and performance tuning.
Good To Have
- Experience with vector search, semantic search, embeddings, or LLM-powered retrieval systems.
- Hands-on experience working with Elasticsearch/Qdrant or any other search engine.
- Exposure to recommendation systems and personalisation.
- Experience in e-commerce search or large-scale consumer platforms.
- Familiarity with ML ranking pipelines and A/B experimentation frameworks.
This job was posted by Priyanka R N from Falabella.
