Machine Learning Engineer

AI Squared; Washington (District of Columbia); On-site; USD 110,000 - 140,000; Full time

Job summary

A leading AI company in Washington, DC is seeking a Machine Learning Engineer to join their core AI team. In this role, you will focus on deploying and monitoring AI systems and work closely with cross-functional teams. The ideal candidate will have over 5 years of experience in a similar role, with strong expertise in cloud platforms and ML lifecycle tools. This position offers competitive compensation and the opportunity to work in a dynamic environment.

Qualifications

• 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
• Proven experience deploying and maintaining machine learning models in production at scale.
• Strong proficiency in Python and familiarity with ML frameworks such as PyTorch or TensorFlow.

Responsibilities

• Design, implement, and maintain ML deployment pipelines.
• Build robust model monitoring, logging, and alerting systems.
• Optimize runtime performance of ML models across cloud platforms.

Skills

Machine Learning model deployment

MLOps practices

Collaboration

Problem-solving

Tools

MLflow

Kubeflow

SageMaker

Docker

Kubernetes

AWS

GCP

Azure

Job description

Overview

We are seeking a highly skilled Machine Learning Engineer to join our core AI team. In this role, you will focus on deploying, maintaining, and monitoring the AI/ML systems that power our platform. You will work closely with data scientists, data engineers, and product teams to ensure scalable, reliable, and production-grade AI solutions. You’ll play a critical role in operationalizing large language models (LLMs) and other ML systems, ensuring they run efficiently, securely, and with robust monitoring in place.

Key Responsibilities

• Design, implement, and maintain ML deployment pipelines for scalable production systems.
• Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.
• Build robust model monitoring, logging, and alerting systems to track performance and detect drift.
• Partner with data scientists to transition models from research/prototype into production-ready deployments.
• Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.
• Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.
• Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.
• Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.

Qualifications

• 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.
• Proven experience deploying and maintaining machine learning models in production at scale.
• Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).
• Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.
• Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.
• Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.
• Strong understanding of MLOps best practices, monitoring, and automation.
• Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.
• Strong communication and collaboration skills across technical and non-technical teams.