Machine Learning Engineer

Machine Learning Engineer

Washington, DC (Hybrid)

About the Role

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.