Machine Learning Engineer
AI SquaredResponsibilities
- Design, implement, and maintain ML deployment pipelines for scalable production systems.
- Operationalize LLMs and other AI/ML models for high availability and reliability.
- Build monitoring, logging, and alerting systems to track model performance and detect drift.
- Transition models from research and prototypes into production-ready deployments with data scientists.
- Develop automated ML workflow deployment pipelines integrating testing and validation.
- Optimize ML model runtime performance across AWS, GCP, Azure, and distributed systems.
- Use Docker and Kubernetes to enable reproducible and scalable ML systems.
- Collaborate with data scientists, data engineers, product teams, and other stakeholders on platform-aligned ML systems.
Requirements
- 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or in a similar role.
- Proven experience deploying and maintaining machine learning models in production at scale.
- Hands-on experience with ML lifecycle tooling such as MLflow, Kubeflow, SageMaker, or Vertex AI.
- Strong proficiency in Python and familiarity with PyTorch or TensorFlow.
- Deep knowledge of Docker and Kubernetes for production ML systems.
- Expertise with AWS, GCP, or Azure for ML deployment and scaling.
- Strong understanding of MLOps best practices, monitoring, and automation.
- Strong problem-solving, communication, and collaboration skills.
Benefits
- Hybrid work arrangement in Washington, DC.
Tech Stack
Categories
About AI Squared
Enterprises and federal agencies are investing heavily in AI, but deployment repeatedly fails at the “last mile” leading to the loss of trillions of dollars in wasted investment and lost opportunity. AISquared solves the last mile problem by creating a secure, production-grade way to operationalize AI inside the business applications where work actually happens, without requiring large new engineering lifts, while giving leaders measurable visibility into adoption, performance, and business impact. AISquared provides a comprehensive, low-code platform, UNIFI and Sparx, designed to “CLOSE” the gap between AI potential and real operational outcomes through five core capabilities: 1. Connects by integrating virtually any data source and any AI model using pre-built connectors. 2. Learns by capturing real-time user feedback directly inside the workflow to support continuous model improvement. 3. Orchestrates by managing complex data workflows and policies through a single UI. 4. Secures deployments with defense-grade controls. 5. Embeds insights where work happens by delivering no-code widgets and visualizations or AI chatbots integrated directly into systems. This results in 5x faster time-to-value and measurable ROI that is Trusted by leading financial institutions, complex supply chain organizations, and the United States Department of Defense, AISquared helps organizations move from stalled pilots to real adoption, faster decision making, and measurable operational impact. Learn more at https://aisquared.ai/ Request a demo at https://aisquared.ai/request-demo/