ML Architect

Responsibilities:

? Design hybrid retrieval systems combining keyword search, vector similarity, and cross-encoder reranking at scale.

? Build intelligent query routing with cascading classification strategies

? Architect multi-model inference pipelines optimized for latency-sensitive workloads

? Define relevance metrics, run A/B experiments, and drive measurable business outcomes

? Support the driving MLOps standards for model deployment, monitoring, and continuous improvement

? Partner with Product, Merchandising, and Engineering to translate business requirements into ML solutions

? Mentor engineers and define search and ML architectural standards

Requirements:

? 7+ years in software, data, or ML engineering with 3+ years building production search systems

? Experience with e-commerce search patterns: faceting, merchandising rules, query understanding

? Strong knowledge of embedding models, approximate nearest neighbor search, and reranking architectures

? Hands-on experience with vector databases and similarity search at scale (Pinecone, Milvus, Weaviate, FAISS or similar)

? MLOps expertise: model deployment pipelines, monitoring, versioning, and retraining workflows

? Production experience with transformer-based models for classification and ranking

? Track record balancing latency, cost, and relevance tradeoffs in real-time systems

? Experience designing controlled experiments and defining ML success metrics