Machine Learning Applications Engineer @ McLean, VA (Hybrid) - NEED LOCALS || W2 Only

• Develop, test, and maintain Python-based ML applications and data workflows.
• Build and support pipelines for ML training, deployment, monitoring, and operations.
• Process and analyze data using Pandas, Polars, or similar Python libraries.
• Support production ML deployments, troubleshooting, performance, and operational workflows.
• Work with AWS and containerized environments to deploy and operate ML solutions.
• Collaborate with engineering and technical teams on ML architecture and deployment strategies.
• Support CI/CD and DevOps processes for ML applications.
• Contribute across both ML development and production/MLOps activities.

Education Requirement - Bachelor’s Degree in: Computer Science, Information Technology, Or related field Machine Learning Applications Engineer Job Summary We’re looking for a hands-on Machine Learning Applications Engineer who can work across ML development, data pipelines, and production deployment. The ideal candidate will have strong Python skills, experience working with data and ML applications, and the ability to support solutions in an AWS cloud environment. Required Skills & Experience * Strong Python programming experience.

• Hands-on experience with Python data-processing libraries such as Pandas or Polars.
• Experience building software, data pipelines, or machine learning solutions using Python.
• Experience working in AWS environments.
• Familiarity with CI/CD, DevOps, and containerization.
• Understanding of deployment, troubleshooting, and production operational workflows.
• Ability to work across both ML development and deployment/operations.
• Strong communication skills with the ability to explain technical concepts and architecture decisions., * Experience with ML orchestration platforms.
• Spark/PySpark for distributed data processing.
• MLOps and production model deployment.
• ML monitoring, observability, and operational support.
• Familiarity with NumPy and scientific computing libraries., The ideal candidate is a software/ML engineer who enjoys working beyond model development and can help take ML solutions into production. They should be comfortable with Python, data pipelines, AWS, deployment, and troubleshooting, with enough MLOps/DevOps knowledge to support the full ML application lifecycle.