Lead ML Engineer

Role Overview

• 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.
• 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.
• 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.
• 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.
• 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.

Requirements

• 10+ years of professional experience in Machine Learning and AI engineering.
• Google Cloud Professional Machine Learning Engineer or TensorFlow Developer Certification.
• Hands-on experience with MLOps, CI/CD pipelines, and orchestration tools (Kubeflow, Airflow, Dagster).
• Familiarity with model serving and monitoring frameworks ( Vertex AI, Azure ML etc)
• Demonstrated track record of mentoring engineers and leading cross-functional AI/ML projects.
• Deep hands-on experience with vector databases (Pinecone, Milvus, Qdrant) and search engines (Elasticsearch, OpenSearch)Proven experience implementing reranking models (BGE, Cohere) and fusion techniques (RRF) in production. Expertise in load testing with Locust or k6 and diagnosing distributed system bottlenecks. Experience integrating enterprise MDM feeds and managing NoSQL stores, specifically Google Cloud Firestore.
• Strong background applying ML techniques to search relevance, ranking, and personalisation. Hands-on experience with LLMs or GenAI for search retrieval and knowledge synthesis.
• Solid grounding in statistical evaluation for search quality and system reliability.

Tech Stack

• Airflow
• Azure
• Cloud
• ElasticSearch
• NoSQL
• Tensorflow