Junior Software Engineer/Data Engineer
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AI Summary
Build and maintain enterprise data architectures, pipelines, and microservices for the US Air Force Research Laboratory. Collaborate with government stakeholders within an Agile framework to transition new technologies to the warfighter.
MTSI is seeking to hire a Junior Software/Data Engineer who will be responsible for building and maintaining enterprise data architectures, pipelines, and microservices for the US Air Force Research Laboratory (AFRL). You will facilitate operations within a secure cloud environment (DevSecOps), actively collaborate with government stakeholders inside an Agile framework, and ensure stakeholder requirements are understood and implemented in a way that enhances the AFRL mission to transition new technologies to the warfighter.
Required Qualifications
- US citizenship and Active Secret Clearance
- 2+ years of experience supporting data engineering, software development, analytics, or related technical environments
- Associates or Bachelor’s degree in Computer Science, Information Systems, Data Science, Engineering, or related technical field
- Experience with Agile development activities
- Experience with Python and modern frontend development systems such as React and Django
- Experience with cloud-native technologies, containerization, or Kubernetes-based environments
- Experience with DevSecOps workflows and version control platforms such as Git
- Comfort with Linux software development environments
- Ability to communicate effectively in remote and in-person team settings
- Active Secret Clearance
Preferred Qualifications:
- Independent self-starter and learner
- Experience operating in a collaborative environment
- Familiarity with Elasticsearch or metadata tagging/search solutions
- Familiarity with CI/CD tools such as ArgoCD
- Familiarity with cloud platforms such as Google Cloud Platform (GCP) or Microsoft Azure
- Familiarity with the full software development lifecycle, including development, testing, deployment, and transition to production environments
- Understanding of AI training, integration, and MLOps.
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