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# Software Engineer (SWE), Level 2 [D.26.0155]

- **Company:** Dover Networks LLC
- **Location:** Dover, MD, United States
- **Experience:** Experienced
- **Salary:** $176,000.0 - $192,000.0
- **Contract:** Permanent contract
- **Skills:** Java (Programming Language), JavaScript (Programming Language), Artificial Intelligence, Amazon Web Services, Confluence, JIRA, Microsoft Azure, Big Data, Signals Intelligence, Cyber Security, Computer Networks, Data Files, DevOps, Systems Analysis, Python (Programming Language), Machine Learning, Software Engineering, Web Applications, Large Language Models, Git, Kubernetes, Atlassian Tools, Bitbucket, Docker, Programming Languages
- **Published:** July 11, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9024334/software-engineer-swe-level-2-d260155

## About the Role

* Experience using AI, LLM's, and leveraging the latest technologies.
 * Production-grade software development experience in Python.
 * Experience with deployment and management of services in cloud-based hosting environments such as AWS/Azure.
 * Service containerization and deployment with Docker/Kubernetes.
 * Familiarity with Git / Bit Bucket.
 * Documentation using Atlassian Tools (Jira, Confluence).

Nice to Haves:

 * Experience with additional programming languages (Java, C).
 * Experience prototyping web applications (JavaScript).
 * Full Stack Development.
 * Analyzing cyber security-related data sets (network traffic, OS events).
 * Knowledge of end-to-end SIGINT collection and analysis systems.

YOE Requirement: 8 yrs., B.S. in a technical discipline or 4 additional yrs. in place of B.S.

## Description

Description: You will act as a general-purpose Software Engineer and Data Integrator for an up-and-coming project where you will design, develop, and integrate solutions involving the analysis of large data sets to provide mission-centric insights. You will be working closely with analysts, data scientists, and other software / system engineers, and systems administrators to integrate AI/ML technologies., * Build upon Docker / Kubernetes experience.

 * Build scripts to convert and normalize data.
 * Utilize DevOps Experience.
 * Leverage AI Experience.
 * Elicit requirements from stakeholders and determine the applicability of Artificial Intelligence and Machine Learning solutions.


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