DevSecOps Platform Engineer, AI Automation
$118,000–$176,000 year
On-site · Dallas, Texas, United States
Job Summary
This hands-on engineering role focuses on building secure CI/CD pipelines and platform automation, maintaining IaC (Terraform, Bicep, CloudFormation) for repeatable environments, and supporting cloud-native apps with Kubernetes. You will integrate SAST/DAST/SCA into delivery pipelines, manage IAM and secrets, and develop AI-enabled capabilities (LLMs, RAG, agent workflows) to enhance diagnostics, remediation, and governance while contributing to observability and incident response. Collaboration with Security, Infra Platform, QA, and SRE teams is expected; the role emphasizes practical delivery, coding, and continuous improvement in a fast-evolving AI-enabled DevSecOps context.
Required Qualifications
- 5+ years of experience in DevSecOps, Platform Engineering, software development, or a closely related engineering role
- Practical experience contributing to CI/CD pipeline engineering (e.g., GitHub Actions, Jenkins, or similar)
- Solid programming skills in Python, Go, or Java
- Working knowledge of at least one major cloud platform (AWS, Azure, or GCP)
- Familiarity with microservices and distributed systems concepts
- Hands-on experience with Infrastructure as Code tools (Terraform, Bicep, or CloudFormation)
- Working knowledge of containers and Kubernetes (deployment, troubleshooting, basic operations)
- Security Understanding of Secure SDLC and DevSecOps practices
- Experience integrating or working with SAST, DAST, and SCA tools in delivery pipelines
- Familiarity with secrets management and IAM concepts and implementation
- Exposure to shift-left security practices, guardrails, and policy-as-code
- AI / GenAI Exposure to or experimentation with LLMs in engineering contexts (e.g., GPT, Azure OpenAI, Claude, Llama)
- Basic understanding of RAG pipelines and how they are applied in practice
- Awareness of agent-based workflow frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen)
- Foundational understanding of embeddings, semantic search, and NLP concepts
- Awareness of LLM risks such as prompt injection and data leakage, and common mitigation patterns
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