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AI Squared

Machine Learning Engineer

Washington, DCmidAdded 1 month ago

About this role

Join a core AI team to deploy and operationalize machine learning systems at scale in a hybrid Washington, DC role. You'll work across the full ML lifecycle—from research to production—ensuring LLMs and other models run reliably, efficiently, and with robust monitoring on cloud platforms.

What you'll do

  • Design and maintain ML deployment pipelines for scalable production environments
  • Operationalize large language models and other AI/ML systems with high availability
  • Build monitoring, logging, and alerting systems to track model performance and detect drift
  • Develop CI/CD pipelines for ML workflows with testing, validation, and automated deployment
  • Partner with data scientists to transition models from prototypes into production-ready deployments
  • Optimize model runtime performance across AWS, GCP, Azure, and distributed systems

What they're looking for

  • Python programming
  • Docker and Kubernetes containerization/orchestration
  • ML frameworks (PyTorch, TensorFlow)
  • Cloud platforms (AWS, GCP, Azure)
  • ML lifecycle tools (MLflow, Kubeflow, SageMaker, Vertex AI)
  • MLOps best practices and automation
  • Model monitoring and observability
  • CI/CD pipeline development
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AI Squared

AI Squared builds AI infrastructure solutions and operationalizes machine learning systems at scale, including LLM deployment and production management. The company is hiring Sales Engineers to sell these solutions to enterprise clients and ML specialists to deploy and maintain machine learning systems across the full lifecycle.

View all jobs at AI Squared

Likely interview questions

  • Walk us through your experience deploying and maintaining ML models in production at scale. What were the biggest challenges you faced?
  • Describe your approach to designing ML deployment pipelines. What tools and frameworks have you used (MLflow, Kubeflow, SageMaker, Vertex AI)?
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