DevSecOps Platform Engineer, AI Automation

About this role

Who are we?

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.

Key Responsibilities

CI/CD & Platform Engineering

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.

Shift-Left Security & Compliance Automation

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.

AI / GenAI-Powered DevSecOps Automation

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.

Observability & Operational Excellence

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.

How You'll Spend Your Time

Time Focus Area

35% Building and enhancing CI/CD pipelines and platform automation

20% Security engineering (scanning tools, policies, compliance)

20% Implementing AI/LLM capabilities (agents, RAG, workflow automation)

15% Cloud infrastructure and Kubernetes support

10% Collaboration (design discussions, reviews, team support)

Required Qualifications (Must Have)

Core Engineering & DevSecOps

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

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.

Preferred Qualifications (Nice to Have)

Awareness of Zero Trust architecture principles. Exposure to AI governance frameworks or compliance automation.

Success Measures - What Great Looks Like

Meaningful contributions to reducing manual security and ops effort through pipeline automation. Consistent delivery of secure, well-tested code and pipeline changes with minimal rework. Active participation in improving adoption of standardized CI/CD patterns across engineering teams. AI capabilities you contribute to operate safely, with governance controls and observable behavior. Demonstrated growth in DevSecOps, cloud, and AI domains over the first 12 months.

Realistic Job Preview - Important to Know

This is a hands-on engineering role - you will be writing code, building pipelines, and debugging real systems daily. You will work in ambiguous, fast-evolving AI environments - expect iteration, experimentation, and learning on the job. A significant portion of effort involves integrating tools and diagnosing complex CI/CD and security issues. AI solutions require ongoing tuning, evaluation, and optimization - they are never fully 'done'. You will balance speed, security, and cost trade-offs while keeping developer workflows practical and usable.

Team & Role Details

Team size: 6-8 engineers across varying seniority levels. Close collaboration with Security, Infrastructure Platform, QA, and SRE teams. Individual Contributor role - no direct reports. Mentorship and growth support provided by senior engineers on the team.

The targeted pay range for this position in the following location is / locations are:

United States - Dallas Infomart Office DAI : 118,000 - 176,000 USD / Annual

Our pay ranges reflect the minimum and maximum target for new hire pay for the full-time position determined by role, level, and location.The pay range shown is…