# Machine Learning Engineer at AI Squared

Machine Learning Engineer at AI Squared in Washington, DC. Hybrid. Posted September 23, 2025.

- Company: AI Squared
- Location: Washington, DC
- Workplace: Hybrid
- Experience: 5+ years
- Posted: 2025-09-23
- Apply: https://job-boards.greenhouse.io/aisquared/jobs/4604010006
- Source: https://jobmagic.ai/jobs/machine-learning-engineer-ai-squared-mv1ddh

## Requirements

- 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.

## Description

**Machine Learning EngineerWashington, 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.