Machine Learning Engineer at AI Sqaured

Washington, District of Columbia, United States

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Not SpecifiedCompensation

Senior (5 to 8 years)Experience Level

Full TimeJob Type

UnknownVisa

Artificial Intelligence, TechnologyIndustries

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

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

Skills

Key technologies and capabilities for this role

PythonPyTorchTensorFlowDockerKubernetesAWSGCPAzureMLflowKubeflowSageMakerVertex AICI/CD

Questions & Answers

Common questions about this position

Is this position remote or hybrid?

The position is hybrid and based in Washington, DC.

What experience level is required for this Machine Learning Engineer role?

Candidates need 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role.

What technical skills are essential for this position?

Key skills include strong proficiency in Python, experience with ML frameworks like PyTorch or TensorFlow, expertise in Docker and Kubernetes, and hands-on experience with cloud platforms such as AWS, GCP, and Azure.

What kind of team will I be working with?

You will work closely with data scientists, data engineers, and product teams as part of the core AI team.

What makes a strong candidate for this role?

A strong candidate has proven experience deploying ML models at scale, hands-on knowledge of ML lifecycle tools like MLflow or Kubeflow, deep MLOps expertise, and excellent communication skills for cross-functional collaboration.

AI Sqaured

Integrates AI models into business applications

About AI Sqaured

Squared.ai focuses on predictive and generative artificial intelligence to enhance business productivity. The company helps businesses integrate AI and machine learning models into their operations, making AI-generated insights more accessible. By bridging the gap between data science and business teams, Squared.ai enables faster launches of AI projects and optimizes machine learning models for real-time integration with business applications. Their services include designing user-friendly experiences and ensuring model performance through real-time monitoring and auto-tuning. This approach allows businesses to gain insights quickly without lengthy development cycles. The goal of Squared.ai is to democratize AI, making it easier for organizations to adopt and leverage AI-driven decision-making.

Washington, District of ColumbiaHeadquarters

2021Year Founded

$19.3MTotal Funding

SERIES_ACompany Stage

Data & Analytics, AI & Machine LearningIndustries

51-200Employees

Risks

Emerging competition from Nvidia's mini supercomputer offering significant AI computing power.

Integration challenges from Multiwoven acquisition may disrupt operations.

Rapid expansion post-funding could lead to overextension and resource strain.

Differentiation

AI Squared bridges data science and business teams for faster AI project launches.

The platform democratizes AI-enhanced decision-making for businesses.

Real-time performance monitoring ensures optimal AI model performance in business applications.

Upsides

Acquisition of Multiwoven enhances data integration capabilities for seamless AI model integration.

Partnership with UNIFI Music expands AI integration services into the music industry.

$13.8M Series A funding boosts AI integration capabilities and platform features.

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