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Director Engineering Machine Learning Jobs in Washington

AI Squared

Machine Learning Engineer

Washington, DC · On-site

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 ...

Phase2 Technology

Machine Learning Engineer

Washington, DC · On-site

$77K - $176K/yr

Required Qualifications * 2+ years of experience with artificial intelligence, data science, or machine learning engineering, including developing and deploying models * Experience with Python coding ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

We are seeking a Machine Learning Engineer with a passion for building mission-critical capabilities to join our talent network. Working at NT Concepts means that you are part of an innovative, agile ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site

$120K - $180K/yr

We are seeking a Machine Learning Engineer with a passion for building mission-critical ... engineering culture that encourages technical growth, leadership, and creative "big idea" problem ...

Booz Allen Hamilton

Machine Learning Engineer

Arlington, VA · On-site

$77K - $176K/yr

You Have: * 2+ years of experience with artificial intelligence, data science, or machine learning engineering, including developing and deploying models * Experience with Python coding and libraries ...

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Machine Learning Engineer

Washington, DC • On-site

AI Squared
See more jobs at AI Squaredexternal

Full-time

Re-posted 24 days ago

Job description

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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