ML Engineer

Role overview Build and improve machine learning solutions from data preparation and experimentation through integration, deployment, and ongoing evaluation. The engineer works with data engineers, software developers, architects, and stakeholders to translate needs into operational capabilities, while documenting methods and supporting iterative delivery in an Agile team.

Responsibilities - Develop, train, test, and optimize models for prediction, classification, and data-informed decisions. - Assess model accuracy, reliability, and performance, then identify improvements and support ongoing monitoring. - Prepare datasets through ingestion, preprocessing, normalization, validation, enrichment, and feature engineering. - Build reusable data pipelines and integrate models with APIs, application services, and other data workflows. - Support CI/CD processes for testing, building, and deploying machine learning capabilities. - Collaborate on requirements, technical designs, planning, and implementation with cross-functional partners. - Document model architecture, experiments, evaluation results, deployment steps, and technical decisions.

Requirements - Bachelor’s degree in computer science, data science, machine learning, artificial intelligence, statistics, mathematics, engineering, or a related technical field; relevant training and experience may be considered as an equivalent combination. - Experience developing and evaluating models with Python and libraries such as scikit-learn, pandas, and NumPy. - Hands-on experience with Databricks for data processing, experimentation, model development, or ML workflows. - Skills in data preparation, feature engineering, validation, and working with structured and semi-structured data. - Familiarity with model deployment, REST APIs, and integration with pipelines or application services. - Experience using Git and GitHub, plus maintaining CI/CD workflows with GitHub Actions. - Understanding of evaluation metrics, testing methods, and model monitoring; strong Python documentation, troubleshooting, and communication skills.

Nice to have - Training or certifications in machine learning, Databricks, cloud technologies, or MLOps. - Experience in government contracting, regulated settings, or large organizations; familiarity with identity and access management, enterprise data platforms, Jira, or Confluence. - The source notes that some contract assignments may require U.S. citizenship or permanent residency held for at least three years, plus federal tax compliance.

Benefits and work setup Benefits are described as including flexible schedules, competitive compensation, and opportunities for training and career development.