Machine Learning Engineer at AISquared

<div class="p-rich_text_section"><strong>Machine Learning Engineer</strong></div> <div class="p-rich_text_section"><strong>Washington, DC (Hybrid)</strong></div> <div class="p-rich_text_section"><br><strong>About the Role:</strong></div> <div class="p-rich_text_section"><br>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.</div> <div class="p-rich_text_section"><br><strong>Key Responsibilities:</strong></div> <ul class="p-rich_text_list p-rich_text_list__bullet p-rich_text_list--nested" data-stringify-type="unordered-list" data-list-tree="true" data-indent="0" data-border="0"> <li data-stringify-indent="0" data-stringify-border="0">Design, implement, and maintain ML deployment pipelines for scalable production systems.</li> <li data-stringify-indent="0" data-stringify-border="0">Operationalize large language models (LLMs) and other AI/ML models, ensuring high availability and reliability.</li> <li data-stringify-indent="0" data-stringify-border="0">Build robust model monitoring, logging, and alerting systems to track performance and detect drift.</li> <li data-stringify-indent="0" data-stringify-border="0">Partner with data scientists to transition models from research/prototype into production-ready deployments.</li> <li data-stringify-indent="0" data-stringify-border="0">Develop CI/CD pipelines for ML workflows, integrating testing, validation, and automated deployment.</li> <li data-stringify-indent="0" data-stringify-border="0">Optimize runtime performance of ML models across cloud platforms (AWS, GCP, Azure) and distributed systems.</li> <li data-stringify-indent="0" data-stringify-border="0">Apply containerization and orchestration (Docker, Kubernetes) to enable reproducible, scalable systems.</li> <li data-stringify-indent="0" data-stringify-border="0">Collaborate with cross-functional teams to ensure ML systems align with platform goals and business requirements.</li> </ul> <div class="p-rich_text_section"><strong>Qualifications:</strong></div> <ul class="p-rich_text_list p-rich_text_list__bullet p-rich_text_list--nested" data-stringify-type="unordered-list" data-list-tree="true" data-indent="0" data-border="0"> <li data-stringify-indent="0" data-stringify-border="0">5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.</li> <li data-stringify-indent="0" data-stringify-border="0">Proven experience deploying and maintaining machine learning models in production at scale.</li> <li data-stringify-indent="0" data-stringify-border="0">Hands-on experience with ML lifecycle tooling (MLflow, Kubeflow, SageMaker, Vertex AI, or similar).</li> <li data-stringify-indent="0" data-stringify-border="0">Strong proficiency in Python; familiarity with ML frameworks such as PyTorch or TensorFlow.</li> <li data-stringify-indent="0" data-stringify-border="0">Deep knowledge of containerization (Docker) and orchestration (Kubernetes) for production ML systems.</li> <li data-stringify-indent="0" data-stringify-border="0">Expertise with cloud platforms (AWS, GCP, Azure) for ML deployment and scaling.</li> <li data-stringify-indent="0" data-stringify-border="0">Strong understanding of MLOps best practices, monitoring, and automation.</li> <li data-stringify-indent="0" data-stringify-border="0">Excellent problem-solving skills, with an emphasis on building reliable, scalable systems.</li> <li data-stringify-indent="0" data-stringify-border="0">Strong communication and collaboration skills across technical and non-technical teams.</li> </ul>