AI/ML Engineer Level 2

CACI•Suitland, MD

17d

About The Position

CACI is seeking a skilled and experienced mid-level AI/ML Engineer to join our dynamic team to support a DoD client in Suitland, MD. The ideal candidate will have a strong background in machine learning, deep learning, and AI system development, with a proven track record of designing, developing, and deploying machine learning models. This role requires a deep understanding of MLOps, containerization, and CI/CD pipelines, as well as advanced knowledge in data preprocessing and cloud environments.

Requirements

TS/SCI Clearance
Bachelor’s degree in Computer Science, Data Science, Mathematics, Statistics, Engineering, or a related field.
3–5 years of experience in machine learning, deep learning, or AI system development.
Experience in MLOps, containerization (e.g., Docker, Kubernetes), and CI/CD pipelines.
Proficiency in designing, developing, and deploying machine learning models.
Advanced knowledge in data preprocessing, including handling large datasets in cloud environments.
Experience in developing custom AI APIs and integrating them into enterprise applications.
Strong knowledge of programming languages for Machine Learning (Python, R, or similar).
Good understanding of AI cloud tools and capabilities.
Familiarity with Responsible AI (RAI) policies and development of metrics, measurements, and evaluation methods for AI.

Nice To Haves

Experience with cloud platforms (Azure, AWS, GCP)
Knowledge of distributed computing frameworks (e.g., Apache Spark)
Familiarity with version control systems (e.g., Git)

Responsibilities

Design, develop, and deploy machine learning models and manage pipelines using tools such as Azure ML, Kubeflow, or MLflow.
Implement and manage MLOps practices, including containerization (e.g., Docker, Kubernetes) and CI/CD pipelines.
Handle large datasets in cloud environments, performing advanced data preprocessing.
Develop custom AI APIs and integrate them into enterprise applications.
Ensure adherence to Responsible AI (RAI) policies and develop metrics, measurements, and evaluation methods for emerging and existing AI areas.
Collaborate with cross-functional teams to identify opportunities for AI/ML integration and improvement.