below marketmost similar roles pay hereabove market
This role pays less than
68%
of similar roles. Most pay
$162,000–$254,750
— the blue band above.
At the midpoint, this role pays about
$186k
versus about
$208k
for comparable roles.
Based on 240 similar postings.
Employer
About General Dynamics
General Dynamics is a global aerospace and defense company offering a broad portfolio of products and services in business aviation, ship construction, land combat vehicles, and information technology. It serves customers in the U.S. government, allied governments, and a diverse array of commercial markets.
General Dynamics currently has
1364 open roles
on FindRole.
Listed pay typically runs
$124,093–$150,385
across 1161 roles with salary data.
The AI/ML Delivery Engineer is a senior practitioner within the data science and data engineering team. This role involves designing, building, and scaling enterprise AI, ML, and data products across cloud environments. You will architect end-to-end solutions, manage data integration, perform feature engineering, and oversee model development, evaluation, and deployment. Key responsibilities include establishing MLOps, LLMOps, and ModelOps practices, defining responsible AI controls, and mentoring engineers. You will utilize modern machine learning and deep learning frameworks, cloud-native services, and distributed data processing tools. The work focuses on delivering secure, scalable solutions for the NIH mission, specifically addressing biomedical research, operational efficiency, and data-driven decision-making. You will handle complex, regulated datasets involving natural language processing, computer vision, predictive analytics, and generative AI applications.
Architect and deliver end-to-end AI/ML and generative AI solutions across the full development lifecycle.
Design scalable, secure, and governed AI/ML architectures for cloud-native services and distributed computing.
Develop and deploy production-ready solutions for large language models, intelligent search, and workflow automation.
Integrate enterprise AI services with organizational data platforms, security controls, and governance frameworks.
Design secure APIs, data pipelines, and orchestration workflows for enterprise-wide AI consumption.
Establish MLOps, LLMOps, and ModelOps practices for CI/CD, experiment tracking, and model versioning.
Define responsible AI controls for privacy, bias assessment, audit logging, and agentic guardrails.
Lead architecture reviews, performance benchmarking, and infrastructure trade-off analysis for large-scale GPU workloads.
What we're looking for
Master of Science in Computer Science, Information Technology, Engineering, Mathematics/Statistics, Bioinformatics, Data Science, or equivalent professional experience.
10+ years of related experience.
Must be a US Person.
Must be able to obtain a Tier 3 Public Trust (NACLC).
Experience with modern enterprise data platforms, cloud-native data architectures, and machine learning lifecycle management.
Production experience developing and deploying AI/ML and generative AI solutions, including LLM applications and workflow automation.
Experience with modern machine learning and deep learning frameworks, libraries, and tools for developing and deploying AI/ML solutions.
Security clearance level : Must be able to obtain a Tier 3 Public Trust.
Artificial Intelligence
Machine Learning
Python
Big Data
Data Transformation
Model Selection
Cross-Validation
Statistical Techniques
Systems Design
Systems Modeling
Simulation
Data Engineering
Data Science
Operating Systems
Application Software