Principal Cloud Engineer

Cloud Engineer Principal

Advance how our customers operate while you advance your career. Join GDIT as a Cloud Engineer Principal and build an impactful career in enterprise IT, collaborating with people who are driven and resourceful like you. As a Cloud Engineer Principal, the work you'll do at GDIT will be impactful to the mission of Healthcare Fraud Prevention Partnership (HFPP) Trusted Third Party (TTP). You will play a crucial role in the mission of fighting fraud waste and abuse.

MEANINGFUL WORK AND PERSONAL IMPACT:

• Design, implement, modernize, and operationally support AWS-based cloud environments and hybrid cloud infrastructure.
• Develop and maintain cloud architecture patterns, reference architectures, reusable templates, and engineering standards for secure, scalable, resilient, and cost-effective platforms.
• Independently drive complex cloud initiatives, owning detailed design and implementation across infrastructure, application support, data engineering, cybersecurity, networking, and operations domains.
• Design, deploy, document, and validate multi-environment cloud infrastructure, including development, test, staging, production, and disaster recovery environments.
• Configure and manage AWS services to meet business, mission, security, availability, and performance requirements.
• Design and manage Amazon VPC environments, including subnets, route tables, network access control lists, security groups, private connectivity, DNS, load balancing, and network segmentation.
• Implement and maintain identity and access management practices, including AWS IAM roles, users, groups, policies, least-privilege access, privileged-access controls, and federation strategies.
• Develop and maintain infrastructure-as-code solutions using AWS CloudFormation, and equivalent automation frameworks.
• Build and improve automation for cloud provisioning, configuration management, image deployment, bootstrapping, scaling, monitoring, remediation, and operational workflows.
• Implement and improve observability capabilities using Amazon CloudWatch and related monitoring, logging, alerting, tracing, and dashboarding tools.
• Perform incident response, root-cause analysis, problem management, capacity planning, performance tuning, and reliability improvements for production cloud environments.
• Implement security-by-design practices, including vulnerability management, encryption, secrets management, logging, audit readiness, security monitoring, and compliance alignment.
• Implement cloud and on-premises integration solutions, including connectivity, identity, data movement, backups, storage, and operational support models.
• Support Linux and Windows environments, clustered file systems, storage, networking, SAN, backup, and large-scale data platforms as applicable.
• Execute technical upgrades, platform enhancements, lifecycle management, and migration activities, including containerization of legacy workloads.
• Develop technical documentation, including architecture diagrams, engineering standards, implementation plans, runbooks, system design documents, operating procedures, and knowledge articles.
• Conduct proof-of-concept efforts and technical evaluations for emerging cloud, automation, data, security, and AI technologies.
• Develop and execute system, integration, performance, security, resiliency, and disaster recovery testing.
• Provide technical mentorship, code and design reviews, and practical guidance to cloud engineers, systems administrators, and DevOps practitioners.
• Communicate technical findings, risks, dependencies, tradeoffs, and recommendations clearly to management, customers, and technical stakeholders.
• Identify and implement opportunities to improve engineering efficiency, platform reliability, customer experience, security posture, automation, and cloud cost management.
• Use and extend AI-assisted engineering workflows, including AI-powered code generation, infrastructure-as-code development, documentation, code review, and operational automation.
• Apply AI-assisted development practices while maintaining security, governance, and quality controls.
• Evaluate and adopt AI-enabled tools-such as Claude Code, Amazon Q Developer, or equivalent approved enterprise tools-to accelerate scripting, infrastructure automation, troubleshooting, and operational workflows.
• Support cloud capabilities for AI and data workloads, including secure data access, compute, storage, networking, model deployment, monitoring, and cost controls using services such as Amazon Bedrock, Amazon SageMaker, or comparable platforms.
• Apply guardrails for responsible AI use, including protection of sensitive data, prompt and output review practices, access controls, audit logging, and human oversight.
• Use AIOps concepts and tools to improve event correlation, anomaly detection, operational triage, predictive capacity planning, and automated remediation.

WHAT YOU'LL NEED TO SUCCEED:

• Bachelor's degree in Computer Science, Information Technology, Engineering, or a related discipline; equivalent combination of education, technical training, and relevant experience may be considered.
• 6-10 years of progressive experience in cloud engineering, systems engineering, DevOps, infrastructure engineering, site reliability engineering, or a related technical discipline.
• Demonstrated hands-on experience designing, deploying, administering, and supporting AWS cloud environments in production.
• Strong experience with AWS services across compute, networking, identity, storage, monitoring, security, and automation domains.
• Experience designing and managing AWS networking, including VPCs, subnets, route tables, security groups, network ACLs, private connectivity, load balancers, DNS, and firewall integration.
• Strong knowledge of AWS IAM, access control, authentication and authorization concepts, least privilege, encryption, secrets management, and cloud security best practices.
• Hands-on experience with infrastructure as code using AWS CloudFormation, Terraform, or similar tools.
• Experience implementing CI/CD pipelines and automated deployment practices.
• Experience with monitoring, logging, alerting, and operational diagnostics using Amazon CloudWatch and related observability tools.
• Proficiency with scripting and programming languages, including Python, Bash, PowerShell, and JavaScript/Node.js.
• Experience administering or supporting Linux and Windows operating systems in enterprise or production environments.
• Strong networking knowledge, including TCP/IP, DNS, TLS, routing, firewall concepts, load balancing, VPNs, network segmentation, and traffic inspection architectures.
• Experience supporting databases and data platforms such as MySQL, PostgreSQL, Oracle, SQL Server, Snowflake, or comparable technologies.
• Experience in a 24/7 production environment, including operational support, incident response, root-cause analysis, and change management.
• Strong written, verbal, and analytical skills with the ability to document complex technical solutions clearly.
• Ability to translate mission, business, security, and operational requirements into practical technical designs and implementation plans.
• Experience applying secure software development, DevSecOps, and automation practices in an Agile or iterative delivery environment.
• Familiarity with enterprise-approved generative AI tools and practical use cases for engineering productivity, including code generation, documentation development, troubleshooting, and infrastructure automation.
• Ability to assess AI-generated code, scripts, infrastructure configurations, and technical recommendations for accuracy, security, maintainability, and operational suitability.

DESIRED QUALIFICATIONS AND EXPERIENCE:

• Bachelor's degree in Computer Science, Engineering, Data Science, or a related field.
• AWS certifications such as AWS Certified Solutions Architect - Professional, AWS Certified DevOps Engineer - Professional, AWS Certified Security - Specialty, AWS Certified Advanced Networking - Specialty, or comparable cloud certifications.
• Experience with multi-account AWS environments, AWS Organizations, Control Tower, landing zones, service control policies, and centralized governance models.
• Experience with Kubernetes and container platforms, including Amazon EKS, ECS, Docker, Helm, and container security practices.
• Experience with configuration management and automation tools such as Ansible, Chef, Puppet, or Systems Manager.
• Experience with enterprise observability platforms such as Datadog, Dynatrace, Splunk, Elastic, Grafana, Prometheus, or OpenTelemetry.
• Experience with cloud security and compliance frameworks, including NIST, CIS benchmarks, SOC 2, HIPAA, or comparable controls.
• Experience supporting healthcare, regulated, or other security-sensitive environments.
• Experience with data engineering and analytics platforms, including Snowflake, Databricks, or equivalent tools.
• Experience implementing FinOps practices, including cloud cost allocation, tagging standards, budget monitoring, rightsizing, capacity optimization, and chargeback/showback reporting.
• Experience supporting AI/ML or generative AI solutions using Amazon Bedrock, Amazon SageMaker, Databricks Machine Learning, or similar platforms.
• Experience implementing AI governance controls, including data classification, model access management, prompt security, audit logging, content filtering, retrieval-augmented generation, and evaluation/monitoring practices.
• Familiarity with AIOps platforms or approaches for anomaly detection, incident correlation, automated remediation, and intelligent operations.
• Experience using AI-assisted developer tools such as Claude Code, Amazon Q Developer, GitHub Copilot, or equivalent organization-