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ML Ops Engineer

ML Ops Engineer

In The LoopMexico
Hace más de 30 días
Descripción del trabajo

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About Us

In short, our technology helps thrift operations price and process items more intelligently. Big picture – we are automating the processing of “single-SKU” items, including the massive problem of returns, through advanced AI and ML.

About Us

In short, our technology helps thrift operations price and process items more intelligently. Big picture – we are automating the processing of “single-SKU” items, including the massive problem of returns, through advanced AI and ML.

Backed by leaders from companies like eBay and Depop, we are scaling quickly and working with clients around the world. This is an opportunity to shape the future of resale.

Position Overview

We’re looking for a part-time MLOps contractor to join our growing technical team. You’ll ensure our models perform under load, maintain the reliability and scalability of our ML systems, and work across infrastructure and data pipelines to support model training, tuning, and deployment.

This is a critical technical role for someone who thrives on performance optimization, infrastructure automation, and model reliability.

Responsibilities

  • Monitor and maintain performance of machine learning models under real-world loads and stress conditions
  • Design, implement, and maintain scalable ML pipelines to support model experimentation, training, and deployment
  • Collaborate with data scientists and backend engineers to streamline model handoff to production environments
  • Identify and evaluate new tools and technologies to improve performance, observability, and maintainability
  • Build automated testing, CI / CD, and model validation processes to ensure model health
  • Help troubleshoot production issues related to ML system behavior

Qualifications

  • 3+ years of experience in MLOps, DevOps, or backend roles with a focus on ML systems
  • Proficient in Python and containerization tools (Docker, Kubernetes)
  • Experience with ML workflow orchestration tools (e.g., MLflow, Airflow, Kubeflow)
  • Familiarity with monitoring, logging, and alerting systems
  • Solid understanding of model lifecycle management and best practices for deploying ML to production
  • Comfortable working independently in a fast-paced, early-stage startup environment

    • Preferred Experience

  • Experience in cloud environments, especially GCP
  • Prior experience in retail, re-commerce, or marketplace technology
  • Hands-on experience with large-scale model serving, batch / real-time inference systems, and A / B testing infrastructure

    • Details

  • Contract Type : Part-time (hourly or fixed weekly commitment)
  • Location : Remote (Eastern or Central Time preferred)
  • Duration : Initial 3–6 months with potential to extend or convert to full-time
  • Compensation : Competitive and commensurate with experience

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    Seniority level

    Seniority level

    Mid-Senior level

    Employment type

    Employment type

    Full-time

    Job function

    Job function

    Engineering and Information Technology

    Industries

    Internet Publishing

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