DevSecOps Platform Engineer, AI Automation

Equinix•Dallas, TX

1d•Onsite

About The Position

Equinix is the world’s digital infrastructure company®, shortening the path to connectivity to enable the innovations that enrich our work, life and planet. A place where tech thinkers and future builders turn bold ideas into breakthrough experiences, we welcome your unique perspective. Help us challenge assumptions, uncover bias, and remove barriers—because progress starts with fresh ideas. You’ll find belonging, purpose, and a team that welcomes you—because when you feel valued, you’re empowered to do your best work.

Requirements

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).
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.
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.

Nice To Haves

Exposure to AIOps tools or observability platforms.
Interest in or familiarity with MLOps pipelines and model lifecycle management.
Experience with QA automation frameworks.
Awareness of Zero Trust architecture principles.
Exposure to AI governance frameworks or compliance automation.

Responsibilities

Contribute to the build, maintenance, and improvement of secure CI/CD pipelines (e.g., GitHub Actions) and reusable workflow templates.
Develop and maintain platform automation that improves developer experience, reliability, and deployment consistency.
Write and maintain Infrastructure as Code (Terraform, Bicep, and/or CloudFormation) for repeatable, consistent environments.
Support cloud-native applications using containers and Kubernetes, including troubleshooting deployments and runtime issues.
Integrate and maintain SAST, DAST, and SCA scanning tools in CI/CD pipelines with actionable reporting and automated gating.
Implement best practices for IAM and secrets management to minimize credential exposure and enforce least privilege.
Contribute to policy-as-code controls aligned to governance requirements.
Partner with Security and engineering teams to align guardrails with practical delivery workflows.
Implement and maintain LLM-enabled capabilities in pipelines and platforms using production-grade LLM services (e.g., GPT, Azure OpenAI, Claude, Llama).
Contribute to RAG pipelines for retrieving runbooks, standards, and historical incident or pipeline context.
Build and support agent-based workflows (LangChain, LangGraph, CrewAI, or AutoGen) to assist with diagnostics and remediation.
Apply LLM risk controls including access boundaries, prompt injection mitigations, and auditability patterns.
Support platform observability and incident response with AI-driven insights and automation.
Participate in tuning and evaluating AI solutions for accuracy, safety, reliability, and cost.
Document standards, patterns, and runbooks to support team knowledge sharing and engineer onboarding.