F

Factored

End-to-end AI talent augmentation services

Senior Machine Learning Operations Engineer

Applied Machine LearningConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • At least 5 years of hands-on MLOps experience, including a proven track record of deploying and scaling machine learning pipelines in enterprise environments.
  • Advanced written and spoken English proficiency.
  • Strong Python and Bash scripting skills, with expertise in Docker containerization and Kubernetes orchestration.
  • Deep hands-on experience with AWS, Google Cloud Platform, or Azure; Terraform; and continuous integration and deployment pipelines.
  • Demonstrated experience with MLOps platforms such as MLflow, DVC, or Kubeflow, and model observability or monitoring solutions such as Prometheus, Grafana, or Arize.

Responsibilities

  • Build and automate continuous integration and deployment pipelines tailored for machine learning models and data workflows, using tools such as GitHub Actions, GitLab CI, or Jenkins.
  • Architect and manage containerized microservices and orchestration platforms for machine learning workloads using Docker and Kubernetes, including EKS, GKE, or AKS.
  • Implement and manage machine learning lifecycle, tracking, and versioning tools such as MLflow, DVC, or Kubeflow.
  • Provision, configure, and maintain machine learning cloud infrastructure using Infrastructure as Code tools such as Terraform.
  • Establish monitoring, alerting, and logging systems for deployed models using tools such as Prometheus, Grafana, or Arize to track data drift, model performance, and operational health.
  • Collaborate with data scientists and software engineers to transition machine learning models from experimental scripts into secure, low-latency, scalable production APIs.

About the Company

Factored

End-to-end AI talent augmentation services

Factored provides end-to-end talent augmentation for AI, ML, and data science by sourcing, vetting, training, and managing remote engineering teams that integrate as extensions of a client's team. Their engineers, mainly from Latin America, work across all US time zones and can provide technical leadership, hands-on execution, or advisory support to accelerate AI roadmaps for US-based companies. They organize experts into Centers of Excellence (AI/ML, Data Science, Data Analytics, Data Engineering, Software Engineering) and apply projects like personalized recommendations, fraud detection, credit risk models, and automated data pipelines. Their differentiation comes from focusing exclusively on AI/data, using a vetted upskilling program developed with Stanford and deeplearning.ai, and offering nearshore talent with seamless integration to clients, with a goal to help organizations scale AI/data capabilities quickly and cost-effectively.

Company size
201-500
Company stage
Seed
Total funding
$2.5M
Headquarters
Mountain View, CA
Founded
2019

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