Sr. Systems Engineer

Abacus Technology•Washington, DC

About The Position

Abacus Technology is seeking a Systems Engineer to provide technical support for the Federal Aviation Administration (FAA). This is a full-time position. Responsibilities Engineer and deploy robust cloud infrastructure solutions across multiple platforms, ensuring solutions are scalable, secure, and meet business requirements. Integrate cloud systems with existing on-premises infrastructure and third-party services and optimize cloud systems for performance, reliability, and cost-efficiency. Develop automation scripts for cloud resource provisioning and management using tools like Terraform, Ansible, or cloud native automation tools and implement continuous integration and continuous deployment (CI/CD) pipeline. Set up and maintain monitoring solutions to ensure system health and performance. Troubleshoot complex issues, conduct root cause analysis, and implement corrective measures. Collaborate with project managers and stakeholders to define project scope, timelines, and deliverables and/or lead technical projects ensuring successful execution and delivery. Develop and maintain comprehensive documentation for cloud systems and processes. Stay updated with the latest cloud technologies and trends to drive innovation. Monitor cloud expenditure and implement strategies for cost optimization and use cloud cost management tools to analyze and forecast cloud spending. Design and deploy advanced AI solutions, integrating machine learning models into cloud architectures. Optimize AI model performance and scalability within cloud environments, ensuring efficient use of resources.

Requirements

10+ years experience in systems engineering.
Bachelor’s degree in Engineering, Mathematics or Science.
Able to troubleshoot high level technical and non-technical issues.
Experience in evaluating, testing, deploying and maintaining both custom developed and third-party software upgrades.
Able in creating and maintaining system architecture and troubleshooting documentation.
Experience with performing engineering and/or scientific research, studies, and analyses of specified technological, scientific, programmatic, and operational issues.
Able to prepare plans and assessments, such as management engineering plans, integrated support plans, and implementation plans.
Must be a US citizen.
Applicants selected will be subject to a U.S. government security investigation and must meet eligibility requirements for access to classified information.

Nice To Haves

Current relevant technical certifications (e.g., Microsoft Certified Solutions Expert (MCSE ) , Associate Systems Engineering Professional (ASEP), Certified Systems Engineering Professional (CSEP)) are strongly desired

Responsibilities

Engineer and deploy robust cloud infrastructure solutions across multiple platforms, ensuring solutions are scalable, secure, and meet business requirements.
Integrate cloud systems with existing on-premises infrastructure and third-party services and optimize cloud systems for performance, reliability, and cost-efficiency.
Develop automation scripts for cloud resource provisioning and management using tools like Terraform, Ansible, or cloud native automation tools and implement continuous integration and continuous deployment (CI/CD) pipeline.
Set up and maintain monitoring solutions to ensure system health and performance.
Troubleshoot complex issues, conduct root cause analysis, and implement corrective measures.
Collaborate with project managers and stakeholders to define project scope, timelines, and deliverables and/or lead technical projects ensuring successful execution and delivery.
Develop and maintain comprehensive documentation for cloud systems and processes.
Stay updated with the latest cloud technologies and trends to drive innovation.
Monitor cloud expenditure and implement strategies for cost optimization and use cloud cost management tools to analyze and forecast cloud spending.
Design and deploy advanced AI solutions, integrating machine learning models into cloud architectures.
Optimize AI model performance and scalability within cloud environments, ensuring efficient use of resources.