Lead-ML Engineer

Search Core & Information Retrieval

Design, build, and tune a hybrid search engine combining Vector Search (semantic) and Lexical Search (BM25/keyword) to deliver best-in-class relevance.

Implement advanced ranking and blending strategies including BGE rerankers and Reciprocal Rank Fusion (RRF).

Own end-to-end search accuracy and relevance metrics (NDCG, MRR, Recall) and drive measurable, data-backed improvements over time.

Evaluation & Quality Assurance

Build and maintain an automated accuracy evaluation harness for continuous, regression-proof pipeline testing.

Establish quality benchmarks and champion a metrics-first engineering culture across the team.

Performance, Scalability & Infrastructure

Conduct systematic load testing (Locust, k6) and stress-test retrieval pipelines to surface and eliminate bottlenecks.

Architect and optimize systems to guarantee a strict SLA of P95 latency < 500ms under peak production load.

Partner with DevOps/MLOps to design scalable, resilient deployment patterns for search and ranking models.

Data Engineering & Governance

Manage ingestion pipelines for Master Data Management (MDM) feed integration, ensuring clean and timely data synchronisation.

Govern schema and operational configurations within the Firestore spec_registry.

Collaborate with data governance teams to uphold data quality standards across all search indexes.

Leadership & Collaboration

Mentor junior and mid-level engineers through code reviews, pairing, and technical guidance.

Lead cross-functional AI/ML project teams, translating business requirements into clear technical roadmaps.

Communicate complex architectural decisions clearly to both technical peers and non-technical stakeholders.