Data Scientist Journeyman — Edge AI for Defense

General Dynamics Information Technology, Inc.; Tampa (FL); On-site; USD 98,000 - 132,000; Full time

Job summary

General Dynamics Information Technology, Inc. (GDIT) is seeking a Data Scientist Journeyman in the USA (FL, MacDill AFB) to develop and deploy AI/ML models.

You will work across edge-to-core systems, leverage open-source libraries, and ensure secure, DoD-compliant data handling in on-premise and hybrid environments. You will collaborate with cross-functional teams to translate requirements into scalable analytics, optimize models for tactical deployment, and advance MLOps practices using MLflow,

Qualifications

• Bachelor degree in CS, ML, DS or related field.
• DoD Directive 8140 compliance.
• 3+ years of related experience.
• Strong Python and ML libraries (PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM).

Responsibilities

• Develop and train AI/ML models using supervised/unsupervised/deep learning.
• Deploy and operationalize models with Docker, Podman, Kubernetes.
• Design edge-compatible ML solutions for low-power or disconnected environments.
• Lead MLOps using MLflow, DVC, Kubeflow; ensure reproducibility and secure lifecycle.
• Collaborate with data scientists, DevSecOps, engineers, analysts for integration.
• Enable real-time sensor data integration for actionable insights.
• Ensure data security and compliance in DoD environments.

Skills

Artificial Intelligence

Development Design

Machine Learning

Modeling

Education

Bachelor Degree in Computer Science, Machine Learning, Data Science, Applied Mathematics, or related technical field

Tools

Docker

Podman

Kubernetes

PyTorch

TensorFlow

Scikit-learn

XGBoost

LightGBM

Job description

General Dynamics Information Technology, Inc. (GDIT) is seeking a Data Scientist Journeyman in the USA (FL, MacDill AFB) to develop and deploy AI/ML models.

You will work across edge-to-core systems, leverage open-source libraries, and ensure secure, DoD-compliant data handling in on-premise and hybrid environments. You will collaborate with cross-functional teams to translate requirements into scalable analytics, optimize models for tactical deployment, and advance MLOps practices using MLflow,