Senior DevSecOps Platform Engineer-AI Automation

Senior DevSecOps Platform Engineer-AI Automation Dallas, TX (hybrid) Perm Role Years of Experience: 9 years minimum Role Summary Client is seeking a Senior DevSecOps / Platform Engineer to design, build, and operate secure CI/CD and platform automation capabilities enhanced with LLM-driven workflows. This hands-on role will embed security and compliance controls into the software delivery lifecycle, implement policy-as-code guardrails, and build AI-powered agents to reduce operational toil, accelerate remediation, and improve enterprise security posture. Time Allocation 30% CI/CD pipeline engineering and platform automation 25% AI/LLM capabilities - agents, RAG, workflow automation 20% Security engineering - scanning tools, policies, compliance 15% Cloud infrastructure and Kubernetes support 10% Architecture reviews, design discussions, cross-team collaboration Key Responsibilities • CI/CD & Platform Engineering • Build, maintain, and improve secure CI/CD pipelines (GitHub Actions) and reusable workflow templates • Develop platform automation for developer experience, reliability, and deployment consistency • Engineer and maintain Infrastructure as Code (Terraform, Bicep, CloudFormation) for repeatable environments • Support cloud-native applications using containers and Kubernetes • Shift-Left Security & Compliance • Integrate SAST, DAST, and SCA scanning tools into CI/CD with actionable reporting and automated gating • Implement best practices for IAM and secrets management; enforce least privilege • Build and maintain policy-as-code controls aligned to governance requirements • Partner with Security and engineering teams to align guardrails with practical delivery workflows • AI / GenAI-Powered DevSecOps • Implement LLM-enabled capabilities using GPT, Azure OpenAI, Claude, and/or Llama in production pipelines • Build and operationalize RAG pipelines for runbooks, standards, and historical incident/pipeline context • Develop agent-based workflows (LangChain, LangGraph, CrewAI, AutoGen) for diagnostics and remediation • Apply LLM risk controls - prompt injection, data leakage mitigation, access boundaries, and auditability • Observability & Operational Excellence • Enhance platform observability and incident response with AI-driven insights and automation • Continuously tune and evaluate AI solutions for accuracy, safety, reliability, and cost • Document standards, patterns, and runbooks; contribute to scalable onboarding and adoption Required Qualifications • Core Engineering & DevSecOps • 8+ years of experience in DevSecOps, Platform Engineering, or related roles • Hands-on expertise with CI/CD pipeline engineering (GitHub Actions) • Strong programming skills in Python, Go, or Java • Deep understanding of cloud platforms: AWS, Azure, or GCP • Strong knowledge of microservices and distributed systems • Infrastructure as Code: Terraform, Bicep, and/or CloudFormation • Containers and Kubernetes • Security • Strong knowledge of Secure SDLC and DevSecOps practices • Experience integrating SAST, DAST, and SCA tools into delivery pipelines • Solid experience with secrets management and IAM concepts • Proven ability to implement shift-left security, guardrails, and policy-as-code • AI / GenAI • Practical experience running LLMs in production (GPT, Azure OpenAI, Claude, Llama) • Experience building RAG pipelines • Experience building agent-based workflows (LangChain, LangGraph, CrewAI, AutoGen) • Understanding of embeddings, semantic search, and NLP fundamentals • Understanding of LLM risks and safe implementation patterns Preferred Qualifications • Experience with AIOps and/or observability platforms • Familiarity with MLOps pipelines and model lifecycle management • Experience with QA automation frameworks • Knowledge of Zero Trust architecture • Exposure to AI governance frameworks and compliance automation What Great Looks Like • Measurable reduction in manual security/ops effort through automation and agentic workflows • Improved application and infrastructure security posture via consistent scanning and policy controls • Faster, more reliable delivery cycles - reduced pipeline failures, shorter time-to-remediate • Increased adoption of secure, standardized CI/CD patterns across engineering teams • AI capabilities operate safely with governance controls, auditability, and cost-aware design Important to Know • This is a hands-on engineering role, not primarily strategy • You will work in ambiguous, fast-evolving AI environments - expect iteration and experimentation • AI solutions require ongoing tuning, evaluation, and optimization • Balance of speed, security, and cost while keeping developer workflows usable Metasys Technologies is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identify, national origin, veteran or disability status.