# Machine Learning Engineer - Company: [Aisquared]() - Location: Washington, DC - Team: Engineering - Posted: September 24, 2025 ## Job description **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. ## Apply [Apply on Aisquared]() Canonical job page: