Machine Learning Engineer

Job overview

The Machine Learning Engineer role is hybrid in Washington, DC. Compensation is not specified. The engineer joins AI Squared's core AI team, working with data scientists, data engineers, and product teams to support the company's platform. The role operationalizes machine-learning systems by building deployment, monitoring, and automation capabilities that make models reliable and scalable in production. It helps move models from research and prototypes into secure, production-grade services aligned with platform and business needs.

What you'll do

• Design and maintain scalable ML deployment pipelines
• operationalize LLMs and other models
• build monitoring, logging, and alerting to track performance and detect drift
• partner with data scientists to deploy models
• develop CI/CD workflows with testing and validation
• optimize model runtime across cloud platforms and distributed systems
• use containerization and orchestration for reproducible, scalable systems
• collaborate across teams to align systems with platform goals.

What we're looking for

Skills & competencies

full-time

machine learning engineer

mlops

ai/ml

large language models

python

pytorch

tensorflow

docker

kubernetes

washington dc

hybrid

aws

gcp

azure

Work arrangement

• Weekend coverage required

Why this role

Requires 5+ years in machine learning engineering, MLOps, or a similar role; production model deployment experience; Python; ML lifecycle tooling; Docker and Kubernetes; and cloud-platform expertise.

About the employer

AI Squared is hiring for this role. Industry: Computer Systems Design Services. Sector: 54.