As a Principal Search Engineer, you will serve as the technical owner and architect of Nasuni's distributed search and MCP SaaS platform. This role is designed for a senior individual contributor who thrives at the intersection of deep technical execution and long-term platform strategy.
This role is for engineers who want to design and operate mission-critical search systems at scale, influence platform direction, and mentor others without people management responsibilities.
Responsibilities:
- Own end-to-end technical design and architectural decisions for the search and MCP platform, including ingestion, indexing, query execution, and relevance tuning.
- Define and evolve scalable OpenSearch-based architectures for multi-tenant SaaS environments.
- Lead performance optimisation to meet SLOs for latency, throughput, and availability.
- Establish best practices for cluster management, observability, and incident response.
- Drive technology selection and architectural decisions while balancing scalability, reliability, and cost.
- Partner with Product and Engineering leadership on long-term technical roadmaps.
- Serve as the primary technical integration point between search and core product teams.
- Mentor and guide engineers through technical reviews and architectural decision-making.
Requirements:
- 10+ years of overall software engineering experience, with 7+ years focused on search systems.
- Hands-on experience with OpenSearch, Elasticsearch, or Solr in production environments.
- Proven ownership of large-scale, multi-tenant search platforms (terabytes of indexed data or more).
- Deep expertise in Java, including high-performance, low-latency services.
- Strong understanding of distributed systems, fault tolerance, and data consistency.
- Experience operating search systems in cloud environments (AWS and/or Azure).
- Production experience with Kubernetes-based deployments.
- Advanced relevance tuning experience (custom analysers, tokenisation, scoring models).
- Experience designing search ingestion pipelines for unstructured data.
- Operational ownership of on-call, incident response, and reliability metrics.
- Working knowledge of Python for scripting, tooling, or data pipelines.
- Experience with Learning to Rank (LTR) or ML-based relevance models.
- Prior ownership of a search platform serving hundreds of customers.
- Experience modernising or replacing legacy search architectures.Contributions to OpenSearch/Elasticsearch ecosystems or internal platform frameworks.