Recommendation system

Build large scale recommendation systems utilizing embeddings generated for structured and unstructured data using methods such as a two tower architecture

Performant recommendation designs which can scale to millions of recommendations per day for different product features

Utilize graph based structures, search and scoring to enhance recommendation quality

Advanced NLP Embedding Systems

Fine-tune (LORA/PEFT), customize and deploy embedding models (LLMs/SLMs) for multi-language text understanding and semantic search

Architect vector search solutions that enable language-agnostic clustering and classification across global datasets

Build and optimize high-performance retrieval systems using vector databases

MLOps Lifecycle Management:

Architect and manage scalable MLOps and LLMOps infrastructure for robust model training, evaluation, deployment, and monitoring systems.

Design comprehensive CI/CD pipelines, implement model monitoring frameworks to identify drift patterns, and ensure high availability and fault tolerance.

Help establish metrics, experimentation frameworks, and statistical validation approaches for AI system performance

Senior Machine Learning Engineer

Responsibilities

Benefits

Company

Zoominfo

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