CLOUD SYSTEMS ENGINEER

Emagine IT•Arlington, VA

5d•Hybrid

About The Position

Emagine IT is seeking a Cloud Systems Engineer to design, build, automate, and operate cloud infrastructure across Amazon Web Services (AWS) and Microsoft Azure. This role focuses on engineering durable, auditable cloud environments rather than primarily responding to operational queues. The Cloud Systems Engineer develops infrastructure as code, automation, and observability capabilities that improve reliability, consistency, security, and compliance. Practical experience applying artificial intelligence (AI) and machine learning to systems engineering workflows - including log triage, infrastructure code generation and review, configuration drift detection, and compliance evidence automation - is highly valued.

Requirements

Seven (7) to ten (10) years of relevant cloud, systems, infrastructure, platform, or DevOps engineering experience, including practical experience operating production workloads in AWS and/or Azure.
Demonstrated command of infrastructure as code as an engineering discipline, including Terraform module design, state management, drift detection, reusable patterns, environment promotion, and troubleshooting when deployed environments diverge from their definitions.
Strong scripting and automation skills using Python, PowerShell, and Bash, with experience maintaining code under version control and code review.
Experience building or maintaining CI/CD pipelines and implementing monitoring, logging, alerting, and operational instrumentation for cloud environments.
Working knowledge of containers, orchestration concepts, Linux administration, and Windows administration.
Working knowledge of NIST SP 800-53, RMF, Zero Trust principles, or comparable security and compliance requirements in federal or other regulated environments.
Ability to document work as a routine engineering practice and to explain technical decisions, tradeoffs, risks, and knowledge gaps clearly to stakeholders outside the immediate discipline.
Ability to obtain and maintain the background investigation or security clearance required for the assigned project, which may include a Public Trust or Secret clearance.

Nice To Haves

Demonstrated infrastructure automation with measurable results, such as reducing provisioning time, decreasing configuration defects, improving consistency across environments, or automatically generating compliance artifacts.
Hands-on experience applying AI or machine learning to systems engineering, operations, security, or compliance workflows, including lessons learned from approaches that did not prove reliable or maintainable.
Experience in federal government, defense, healthcare, financial services, or other regulated technology environments.
Experience with Kubernetes, Ansible, GitOps workflows, local model deployment, or open-source contribution.
Relevant certifications such as AWS Certified Solutions Architect, Microsoft Certified: Azure Administrator Associate, HashiCorp Certified: Terraform Associate, or CompTIA Security+.

Responsibilities

Design, deploy, maintain, and improve cloud environments across AWS and Azure using infrastructure as code, primarily Terraform, with consistent patterns across development, test, and production.
Develop production-quality automation and systems tooling in Python, PowerShell, and Bash using version control, peer review, testing, and documented development practices.
Build, maintain, and improve CI/CD pipelines used to validate, deploy, and manage infrastructure and configuration changes.
Implement monitoring, logging, alerting, and observability capabilities that identify failures, performance issues, and configuration drift before they materially affect users.
Apply federal security and compliance requirements, including NIST SP 800-53, the Risk Management Framework (RMF), and Zero Trust principles, to cloud architecture and operations.
Automate the collection, generation, validation, and maintenance of compliance evidence and other recurring security artifacts where practical.
Use AI and machine learning capabilities responsibly to improve systems engineering workflows, such as triaging logs, reviewing or generating infrastructure code, identifying anomalies or drift, and accelerating compliance-related analysis.
Document cloud architectures, automation patterns, operating procedures, and technical decisions; communicate design choices, risks, and limitations clearly to technical and non-technical stakeholders.
Participate in troubleshooting, root-cause analysis, reliability improvement, and continuous modernization of cloud platforms and supporting tooling.