ML Operations Engineer

Summary:
We are seeking a Machine Learning Operations (ML Ops) Engineer to join our team. This role will be crucial in scaling and maintaining the ML infrastructure that powers Refine’s multimodal search engine and recommendation systems.

Key Responsibilities:

• Design, implement, and manage scalable ML pipelines for model training, deployment, and monitoring.

• Automate data preprocessing, feature engineering, and model validation workflows.

• Collaborate with data scientists and backend engineers to productionize ML models.

• Ensure the reliability, security, and efficiency of ML infrastructure.

• Optimize performance across training and inference pipelines.

Qualifications:

• Proven experience with ML Ops practices, including MLflow, Kubeflow, or similar platforms.

• Deep familiarity with Python, TensorFlow, PyTorch, or similar frameworks.

• Strong experience with cloud infrastructure (AWS, GCP preferred).

• Background in CI/CD and containerization (Docker, Kubernetes).

• Familiarity with large language models and vector databases is a plus.

Salary Range:
$150,000–$175,000 USD base salary range, depending on experience and fit.

About Refine
Refine is pioneering the next generation of search and discovery technology. We empower brands to transition from outdated, keyword-based search systems to cutting-edge AI-powered solutions. Our proprietary multimodal search model, R4E, integrates seamlessly with e-commerce platforms, enabling natural language queries and personalized search experiences. Starting with fashion e-commerce, Refine’s vision is to lead the industry in product search, recommendation systems, and data pipelines for large language models and AI agents.

Over the next few years, Refine aims to expand its reach across all e-commerce verticals, becoming the industry leader in AI-driven product search and recommendation systems. Our ultimate goal is to unify personalization and create data pipelines that drive innovation across AI platforms, transforming how consumers and businesses interact with digital storefronts.