Machine Learning Engineer
Washington, DC · last year
You will deploy, maintain and monitor LLMs and other ML models in production, building pipelines, CI/CD and monitoring systems. You will work with data scientists to move models from prototype to reliable, scalable deployments.
- Workplace
- Hybrid
- Seniority
- Senior
- Experience
- 5+ years
- Team
- Engineering
Skills
About the role
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.
Posted Sep 24, 2025