ML Architect

Saxon Global • Birmingham, AL

1d

About The Position

We are seeking an experienced ML Architect to design and build hybrid retrieval systems that combine keyword search, vector similarity, and cross-encoder reranking at scale. You will be responsible for building intelligent query routing with cascading classification strategies and architecting multi-model inference pipelines optimized for latency-sensitive workloads. This role involves defining relevance metrics, running A/B experiments, and driving measurable business outcomes. You will also support the driving of MLOps standards for model deployment, monitoring, and continuous improvement. A key aspect of this role is partnering with Product, Merchandising, and Engineering to translate business requirements into ML solutions, and mentoring engineers while defining 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.

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.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed