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Added 6 mins ago
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- $150,000 to $180,000 USD / year
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Cloud Platform Engineer
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- $150,000 to $190,000 USD / year
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Credence

AI Platform & DevSecOps Engineer

Remote, United States

$150,000 to $190,000 USD / year

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Job details

Pay: $150,000 to $190,000 USD / year
Experience: 5+ years of hands-on experience in DevSecOps, DevOps, cloud engineering, platform engineering, software engineering, or related technical roles.
Posting language: English
Posted: Sep 24, 2026

Original job description

Description

Overview

Join a team where innovation meets mission. Our AI, cloud, cyber, and modernization solutions save agencies thousands of hours, safeguard national security, and strengthen health and humanitarian missions worldwide. With 1,700+ team members, 1,500+ AI/data experts, and 100+ prime contracts, we deliver at scale and with purpose. We’ve been recognized as a Top Workplace by the Washington Post for six straight years and named to the Inc. 5000 Fastest Growing Private Companies 13 of the past 14 years. Credence is a welcoming home for those looking to grow and contribute to positive change. We encourage all employees to expand beyond their boundaries, dive into important world-changing Federal challenges. Credence has an immediate need for an AI Platform & DevSecOps Engineer to support federal programs hosted within AWS GovCloud. This hands-on engineering role combines DevSecOps, cloud infrastructure, platform engineering, security automation, and AI platform enablement to support secure and reliable delivery of mission-critical applications. The engineer will design, automate, secure, and operate cloud infrastructure, CI/CD pipelines, container platforms, and shared platform services while supporting the infrastructure and operational capabilities needed for AI-enabled applications and services. The ideal candidate has a strong foundation in AWS, Kubernetes, Infrastructure as Code (IaC), CI/CD, Python, security automation, and production operations, along with practical knowledge of modern AI technologies and the infrastructure patterns used to securely deploy and operate AI-enabled solutions. Core Responsibilities DevSecOps & Platform Engineering: Design, build, and maintain secure, scalable platform capabilities supporting application development and delivery within AWS GovCloud. Develop reusable infrastructure, automation, deployment patterns, and platform services that improve engineering consistency, security, and developer productivity. AWS GovCloud Architecture & Management: Design, implement, and maintain secure, scalable, and compliant AWS GovCloud environments supporting DoD and federal applications. Work with AWS services including Amazon EKS, ECS/Fargate, IAM, VPC, S3, CloudWatch, and other approved cloud-native services. DevSecOps Pipeline Development: Build, maintain, and improve CI/CD pipelines using technologies such as GitLab CI/CD, Jenkins, AWS CodePipeline, and related tooling. Automate application and infrastructure deployments while integrating testing, security scanning, compliance checks, and deployment controls into delivery workflows. Infrastructure as Code (IaC): Automate infrastructure provisioning and configuration using technologies such as Terraform, CloudFormation, and Ansible. Develop reusable infrastructure components and apply version control, peer review, automated validation, and repeatable deployment practices. Containerization & Orchestration: Deploy and operate containerized applications using Docker/OCI containers and Kubernetes, including Amazon EKS. Support workload configuration, networking, storage, secrets, scaling, resource management, upgrades, security, and troubleshooting. Security & Compliance: Integrate security throughout the software development lifecycle and support compliance with applicable federal cybersecurity frameworks, including NIST 800-53, NIST 800-171, RMF, FedRAMP, STIGs, and Zero Trust principles. Implement and support automated security capabilities including SAST/DAST, software composition analysis, container scanning, IaC scanning, secrets detection, vulnerability management, and compliance validation. AI Platform Enablement: Support the cloud infrastructure and shared platform capabilities required to securely deploy and operate AI-enabled applications and services. Work with AI and software engineering teams to establish repeatable patterns for deployment, configuration, access control, security, monitoring, scalability, and operational support. AI/LLM Service Integration: Support the secure integration and operation of approved AI and LLM services, including platforms such as Amazon Bedrock and other enterprise AI services. Assist engineering teams with infrastructure, authentication and authorization, API connectivity, configuration, deployment automation, monitoring, and operational troubleshooting. Monitoring, Observability & Incident Response: Implement monitoring, logging, tracing, dashboards, and alerting across cloud infrastructure, Kubernetes, applications, and platform services using technologies such as AWS CloudWatch, Security Hub, GuardDuty, Splunk/ELK, Prometheus, Grafana, OpenTelemetry, or comparable solutions. Participate in incident response, root-cause analysis, vulnerability remediation, and continuous reliability improvements. Automation & Scripting: Develop automation and platform tooling using Python, Bash, PowerShell, or similar technologies to eliminate repetitive operational tasks, integrate services and APIs, enforce platform standards, and improve deployment efficiency. Production Operations: Troubleshoot complex infrastructure, application, CI/CD, Kubernetes, networking, and cloud service issues in production and production-like environments. Support operational readiness, system reliability, capacity, performance, and continuous improvement. Collaboration & Knowledge Sharing: Work closely with software engineers, AI engineers, cybersecurity teams, cloud engineers, architects, and mission stakeholders to integrate security, automation, and platform capabilities throughout the software development lifecycle. Develop technical documentation, architecture diagrams, runbooks, and reusable engineering standards.

Why This Role Matters Mission Impact - Build and operate secure cloud and DevSecOps capabilities that directly support mission-critical federal applications and emerging AI-enabled solutions. Secure AI Enablement - Help bridge the gap between AI development and production operations by providing secure, automated, observable, and repeatable platform capabilities for deploying AI-enabled applications within federal environments. Modern Platform Engineering - Work across AWS GovCloud, Kubernetes, Infrastructure as Code, CI/CD, automation, cybersecurity, observability, and emerging AI technologies while helping modernize how applications are delivered and operated. Cross-Functional Engineering - Collaborate with DevSecOps, software, AI, cloud, and cybersecurity engineers to solve complex technical challenges without being limited to a single technology domain. Continuous Growth - Expand existing DevSecOps and cloud expertise into AI platform engineering while continuing to deepen skills in automation, security, cloud-native technologies, and production operations.

What You Bring

Bachelor’s degree in Computer Science, Information Technology, Engineering, Cybersecurity, or a related technical field, or equivalent relevant professional experience.
5+ years of hands-on experience in DevSecOps, DevOps, cloud engineering, platform engineering, software engineering, or related technical roles.
Strong hands-on experience designing, deploying, or operating workloads within AWS; AWS GovCloud experience is highly desirable.
Hands-on experience with Docker/OCI containers and Kubernetes, preferably Amazon EKS.
Experience developing and maintaining CI/CD pipelines using GitLab CI/CD, Jenkins, AWS CodePipeline, or comparable technologies.
Strong experience with Infrastructure as Code, including Terraform, CloudFormation, or comparable technologies.
Proficiency with Python and experience using Bash, PowerShell, or similar scripting languages for automation.
Experience with Git-based development workflows, automated testing, artifact management, and modern software delivery practices.
Experience integrating security capabilities into CI/CD and cloud environments, including vulnerability scanning, secrets management, IAM, secure configuration, and automated security controls.
Experience implementing monitoring, logging, alerting, and operational troubleshooting for production or production-like environments.
Working knowledge of AI/ML and generative AI technologies, including LLMs, AI APIs, model inference, and cloud infrastructure supporting AI-enabled applications.
Experience supporting, deploying, integrating, or operating AI/ML or AI-enabled applications in cloud or containerized environments.
Understanding of cloud security principles, identity and access management, networking, encryption, secrets management, and least-privilege access.
Strong troubleshooting, technical communication, documentation, and cross-functional collaboration skills.
U.S. Citizenship with eligibility for a DoD Secret clearance.

Preferred

Experience supporting DoD, federal civilian, or other highly regulated environments.
Hands-on experience with AWS GovCloud.
Knowledge of federal cybersecurity requirements and frameworks including RMF, NIST 800-53, NIST 800-171, FedRAMP, DISA STIGs, and Zero Trust.
Experience with Amazon Bedrock, Amazon Sagemaker, or other enterprise AI/ML platforms and services.
Familiarity with operational considerations for AI workloads, including security, observability, scalability, availability, performance, and cost.
Experience implementing CI/CD or platform automation supporting AI-enabled applications and services.
Familiarity with AI security and governance considerations within federal or regulated environments.
Experience with Helm, Argo CD, Flux, or other Kubernetes/GitOps technologies.
Experience with observability technologies such as CloudWatch, Prometheus, Grafana, OpenTelemetry, Splunk, or ELK.
Experience with software supply-chain security practices such as SBOM generation, artifact signing, provenance, container hardening, and policy enforcement.
Experience with secrets-management and security technologies such as AWS Secrets Manager, AWS KMS, HashiCorp Vault, or comparable solutions.
AWS, Kubernetes, security, DevSecOps, or AI/ML-related professional certifications.

Salary Range $150,000 - $190,000 annually. Actual compensation will be determined based on the selected candidate’s experience, education, certifications, skills, location, and overall qualifications.

Health Care Plan (Medical, Dental & Vision)
Retirement Plan (401k, IRA)
Life Insurance (Basic, Voluntary & AD&D)
Paid Time Off (Vacation, Sick & Public Holidays)
Family Leave (Maternity, Paternity)
Short Term & Long Term Disability
Training & Development
Wellness Resources

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