Company

AI Squared

At AI Squared, our vision is to foster widespread AI adoption by embedding AI insights directly into mission-critical business applications and everyday workflows. By leveraging AI Squared’s platform, organizations can access and integrate any data or machine-learning insight directly into their web-based business applications – leading to data-driven decisions and ML-powered innovation.

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Machine Learning Engineer

AI Squared

Washington, DC Engineering 5mos ago

Data Scientist

AI Squared

Washington, DC Engineering 5mos ago

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Machine Learning Engineer

AI Squared · Washington, DC

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Engineering HQ Posted Nov 26, 2025

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.

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Data Scientist

AI Squared · Washington, DC

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Engineering HQ Posted Nov 26, 2025

Data Scientist

Washington, DC (Hybrid)

About the Role:

We are looking for a highly motivated Data Scientist with a strong background in applied machine learning and AI to join our growing team. In this role, you will be a key contributor to the development of core AI/ML solutions that power our platform. You will collaborate closely with product and engineering teams, applying state-of-the-art techniques to solve complex challenges, advance our use of large language models (LLMs), and ensure scalable, production-ready solutions.

Key Responsibilities:

Leverage 5+ years of experience in data science to design, implement, and optimize machine learning models and pipelines.
Develop, fine-tune, and evaluate large language models (LLMs) for a variety of applications, ensuring accuracy, performance, and robustness.
Collaborate with engineering and product teams to integrate AI/ML solutions into our platform in a scalable and maintainable way.
Conduct applied research, staying current on advances in LLMs, generative AI, and data science methodologies, and translate them into practical solutions.
Build end-to-end workflows, from data exploration and feature engineering to training, validation, deployment, and monitoring in production.
Apply modern containerization and orchestration techniques (e.g., Docker, Kubernetes) to support reproducible experimentation and deployment.
Work with cloud platforms (e.g., Databricks, AWS, GCP, Azure) to manage data pipelines, large-scale training jobs, and distributed systems.
Collaborate across teams to ensure our AI capabilities align with platform goals and business needs.

Qualifications:

5+ years of experience as a Data Scientist or Machine Learning Engineer, with proven success in deploying models to production.
Hands-on experience with large language models (LLMs); fine-tuning experience strongly preferred.
Strong background in Python and ML frameworks such as PyTorch or TensorFlow.
Proficiency in containerization and orchestration technologies (Docker, Kubernetes).
Experience with cloud platforms and ML ecosystems (Databricks, AWS, GCP, Azure).
Familiarity with MLOps best practices, including model deployment, monitoring, and CI/CD for ML.
Strong analytical and problem-solving skills, with the ability to translate research into production-ready solutions.
Excellent communication and collaboration skills, with the ability to work effectively across product, engineering, and leadership teams.
A proactive, self-starter mindset with a passion for applied research and innovation.

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AI Squared

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