Senior DevSecOps Platform Engineer, AI Automation

Equinix•Dallas, TX

2d•$155,000 - $233,000•Onsite

About The Position

We are 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. You’ll collaborate closely with Security, SRE/Infra Platform, and engineering teams to enable faster, safer software delivery while balancing speed, security, and cost.

Requirements

8+ years experience in DevSecOps / Platform Engineering or related roles.
Hands-on expertise with CI/CD pipeline engineering (e.g., 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.
Strong knowledge of Infrastructure as Code (Terraform, Bicep, CloudFormation).
Strong knowledge of containers and Kubernetes.
Strong knowledge of Secure SDLC / DevSecOps practices.
Experience integrating SAST, DAST, and SCA tools into delivery pipelines.
Solid experience with secrets management and IAM concepts and implementation.
Proven ability to implement shift-left security, guardrails, and policy-as-code.
Practical experience running LLMs in production (e.g., GPT, Azure OpenAI, Claude, Llama).
Experience building RAG pipelines.
Experience building agent-based workflows (e.g., LangChain, LangGraph, CrewAI, AutoGen).
Understanding of embeddings, semantic search, and NLP fundamentals.
Understanding of LLM risks (prompt injection, data leakage) and safe implementation patterns.

Nice To Haves

Experience with AIOps and/or observability platforms.
Familiarity with MLOps pipelines and model lifecycle management.
Experience with synthetic data generation/anonymization.
Experience with QA automation frameworks.
Knowledge of Zero Trust architecture.
Exposure to AI governance frameworks and compliance automation.

Responsibilities

Build, maintain, and continuously improve secure CI/CD pipelines (e.g., GitHub Actions) and reusable workflow templates.
Develop platform automation that improves developer experience, reliability, and deployment consistency.
Engineer and maintain Infrastructure as Code (Terraform, Bicep, and/or CloudFormation) for repeatable environments.
Support cloud-native applications using containers and Kubernetes, including troubleshooting deployments and runtime issues.
Integrate SAST, DAST, and SCA scanning tools into CI/CD with actionable reporting and automated gating.
Implement best practices for IAM and secrets management, minimizing credential exposure and enforcing least privilege.
Build and maintain policy-as-code controls that align to governance requirements and reduce manual compliance effort.
Partner with Security and engineering teams to align guardrails with practical delivery workflows.
Implement LLM-enabled capabilities in pipelines and platforms using production-grade LLM services (e.g., GPT, Azure OpenAI, Claude, Llama).
Build and operationalize RAG pipelines for retrieving runbooks, standards, and historical incident/pipeline context.
Develop agent-based workflows (LangChain, LangGraph, CrewAI, AutoGen) to assist with diagnostics and remediation.
Develop agents that leverage code, logs, pipeline signals, and security findings to diagnose CI/CD failures, recommend fixes, and automate safe recovery actions within defined guardrails.
Apply LLM risk controls and mitigations (e.g., prompt injection, data leakage) including access boundaries and auditability.
Enhance platform observability and incident response by integrating 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.