Responsibilities
- Design and implement distributed backend services that power real-time search and discovery across multiple product verticals.
- Build and optimise indexing, retrieval, and ranking pipelines (e.g., combining BM25 keyword retrieval with vector embeddings and personalisation signals).
- Partner with ML, Infrastructure, and Product teams to evolve search relevance, ranking, and experimentation frameworks.
- Architect and evolve API contracts to support multiple clients (web, mobile, internal tools) with low-latency, high-availability performance.
- Drive technical direction for Search — influencing architecture, observability, scalability, and cost efficiency.
- Contribute to a shared Search platform that other verticals (Sports, Casino, etc.) can extend and build upon.
- Mentor engineers across the Explore tribe, raising the bar for backend design and operational excellence.
Requirements
- 6+ years of backend engineering experience, preferably in distributed systems, data platforms, or search domains.
- Deep knowledge of one or more backend languages (Python, Kotlin, Java, Go, Elixir) and proficiency in API design and microservice architecture.
- Experience with search technologies such as OpenSearch, Elasticsearch, Lucene, or vector databases (e.g. Pinecone or FAISS).
- Familiarity with indexing pipelines, ranking models, embeddings, or relevance tuning.
- Hands-on experience with streaming and data processing frameworks (Kafka, Spark, Flink, Airflow, or similar).
- Solid understanding of scalability, caching, and performance optimisation in high-traffic systems.
- Experience integrating backend services into CI/CD pipelines and monitoring via Datadog, Grafana, or similar tools.
- Strong communication and collaboration skills, with the ability to work effectively in cross-functional teams.
- Bachelor's or Master's degree in Computer Science or a related field.
Nice to Have
- Experience designing or operating multi-tenant or cross-domain platforms.
- Background in search relevance, feature stores, or personalisation pipelines.
- Exposure to experimentation and A/B testing frameworks.
- Comfort with infrastructure-as-code (Terraform, AWS CDK) and container orchestration (Kubernetes).
