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Senior AI Platform Engineer - Model Reliability

Sofia Reyes

Full-time · Senior · New_York

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About the role

This is the role for someone who is a little tired of hearing about AI strategy and a lot more interested in why the deployment failed at 2:13 a.m. Our customers run forecasting and document models in production, and what they buy from us is trust. We need a senior platform engineer who can make model delivery boring in the best possible way: repeatable, observable, and hard to break.

Responsibilities

  • Improve the deployment standards used by all production ML services
  • Strengthen observability around model latency, drift, and failure recovery
  • Work with applied teams to reduce release risk before launch
  • Standardise rollback and incident response procedures across environments
  • Mentor engineers who are moving from experimentation into production ownership

Requirements

  • 6+ years in platform, infrastructure, or MLOps engineering
  • Python for automation and internal tooling
  • Kubernetes in production environments
  • AWS services such as EKS, IAM, CloudWatch, and S3
  • MLflow or similar model release tooling
  • CI/CD ownership for deployment and rollback workflows

Benefits

  • $150,000 - $176,000 base salary
  • Remote within the US
  • Equity grant and annual bonus
  • Strong healthcare and retirement match
  • A team that values operational excellence over hype

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Categories

MLOps & AI Infrastructure

Skills

python kubernetes aws mlflow ci/cd

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Job Type

Full-time

Level

Senior

Language

English

Salary Range

$150,000 - $176,000

AI Expertise

MLOps & AI Infrastructure

Skills

python kubernetes aws mlflow ci/cd

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