Roles & Responsibilities
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 bold ideas are welcomed, human connection is valued, and everyone has the opportunity to shape their future.
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.
We’re looking for a Senior Cloud Engineer with a strong foundation in Multi-Cloud & multi region deployment, data architecture, distributed systems, and modern cloud-native platforms to architect, build, and maintain intelligent infrastructure and systems that power our AI, GenAI and data-intensive workloads.
You’ll work closely with cross-functional teams, including data scientists, ML & software engineers, and product managers & play a key role in designing a highly scalable platform to manage the lifecycle of data pipelines, APIs, real-time streaming, and agentic GenAI workflows, while enabling federated data architectures. The ideal candidate will have a strong background in building and maintaining scalable AI & Data Platform, optimizing workflows, and ensuring the reliability and performance of Data Platform systems.
Responsibilities
Cloud Architecture & Engineering
- Deep expertise in designing, implementing, and managing architectures across multiple cloud platforms (e.g., AWS, Azure, GCP)
- Proven experience in architecting hybrid and multi-cloud solutions, including interconnectivity, security, workload placement, and DR strategies
- Strong knowledge of cloud-native services (e.g., serverless, containers, managed databases, storage, networking)
- Experience with enterprise-grade IAM, security controls, and compliance frameworks across cloud environments
AI & GenAI Platform Integration
Integrate LLM APIs (OpenAI, Gemini, Claude, etc.) into platform workflows for intelligent automation and enhanced user experienceBuild and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, or AutoGen for use cases such as pipeline debugging, code generation, and MLOpsExperience in developing and integrating GenAI applications using MCP and orchestration of LLM-powered workflows (e.g., summarization, document Q&A, chatbot assistants, and intelligent data exploration)Hands-on expertise building and optimizing vector search and RAG pipelines using tools like Weaviate, Pinecone, or FAISS to support embedding-based retrieval and real-time semantic search across structured and unstructured datasetsEngineering Enablement
Create extensible CLIs, SDKs, and blueprints to simplify onboarding, accelerate development, and standardize best practicesStreamline onboarding, documentation, and platform implementation & support using GenAI and conversational interfacesCollaborate across teams to enforce cost, reliability, and security standards within platform blueprints.Work with engineering by introducing platform enhancements, observability, and cost optimization techniquesFoster a culture of ownership, continuous learning, and innovationAutomation, IaC, CI / CD
Mastery of Infrastructure as Code (IaC) tools — especially Terraform, Terragrunt, and CloudFormation / ARM / Deployment ManagerExperience building and managing cloud automation frameworks (e.g., using Python, Go, or Bash for orchestration and tooling)Hands-on experience with CI / CD pipelines (e.g., GitHub Actions) for cloud resource deploymentsExpertise in implementing policy-as-code & Compliance-as-code (e.g., Open Policy Agent, Sentinel)Security, Governance & Cost
Strong background in implementing cloud security best practices (network segmentation, encryption, secrets management, key management, etc.).Experience with multi-account / multi-subscription / multi-project governance models, including landing zones, service control policies, and organizational structuresAbility to design for cost optimization, tagging strategies, and usage monitoring across cloud providersMonitoring & Operations
Familiarity with cloud monitoring, logging, and observability tools (e.g., CloudWatch, Azure Monitor, GCP Operations Suite, Datadog, Prometheus)Experience with incident management and building self-healing cloud architecturesPlatform & Cloud Engineering
Develop and maintain real-time and batch data pipelines using tools like Airflow, dbt, Dataform, and Dataflow / SparkDesign and develop event-driven architectures using Apache Kafka, Google Pub / Sub, or equivalent messaging systemsBuild and expose high-performance data APIs and microservices to support downstream applications, ML workflows, and GenAI agentsArchitect and manage multi-cloud and hybrid cloud platforms (e.g., GCP, AWS, Azure) optimized for AI, ML, and real-time data processing workloadsBuild reusable frameworks and infrastructure-as-code (IaC) using Terraform, Kubernetes, and CI / CD to drive self-service and automationEnsure platform scalability, resilience, and cost efficiency through modern practices like GitOps, observability, and chaos engineeringLeadership & Collaboration
Experience leading cloud architecture reviews, defining standards, and mentoring engineering teamsAbility to work cross-functionally with security, networking, application, and data teams to deliver integrated cloud solutionsStrong communication skills to engage stakeholders at various levels, from engineering to executivesQualifications
10+ years of hands-on experience in Platform or Data Engineering, Cloud Architecture, Multi-Cloud Multi-Region Deployment & Architecture, AI Engineering rolesStrong programming background in Java, Python, SQL, and one or more general-purpose languagesDeep knowledge of data modeling, distributed systems, and API design in production environmentsProficiency in designing and managing Kubernetes, serverless workloads, and streaming systems (Kafka, Pub / Sub, Flink, Spark)Experience with metadata management, data catalogs, data quality enforcement, and semantic modeling & automated integration with Data PlatformProven experience building scalable, efficient data pipelines for structured and unstructured dataExperience with GenAI / LLM frameworks and tools for orchestration and workflow automationExperience with RAG pipelines, vector databases, and embedding-based searchFamiliarity with observability tools (Prometheus, Grafana, OpenTelemetry) and strong debugging skills across the stackExperience with ML Platforms (MLFlow, Vertex AI, Kubeflow) and AI / ML observability toolsPrior implementation of data mesh or data fabric in a large-scale enterpriseExperience with Looker Modeler, LookML, or semantic modeling layersPreferred Certifications
AWS Certified Solutions Architect – ProfessionalGoogle Professional Cloud ArchitectMicrosoft Certified : Azure Solutions Architect ExpertHashiCorp Certified : Terraform AssociateOther relevant certifications (CKA, CKS, CISSP cloud concentration) are a plus.Why You’ll Love This Role
Drive technical leadership across AI-native data platforms, automation systems, and self-service toolsCollaborate across teams to shape the next generation of intelligent platforms in the enterpriseWork with a high-energy, mission-driven team that embraces innovation, open-source, and experimentationEquinix is an Equal Employment Opportunity and, in the U.S., an Affirmative Action employer. All qualified applicants will receive consideration for employment without regard to unlawful consideration of race, color, religion, creed, national or ethnic origin, ancestry, place of birth, citizenship, sex, pregnancy / childbirth or related medical conditions, sexual orientation, gender identity or expression, marital or domestic partnership status, age, veteran or military status, physical or mental disability, medical condition, genetic information, political / organizational affiliation, status as a victim or family member of a victim of crime or abuse, or any other status protected by applicable law.
Tell employers what skills you have
Security Governance
Scalability
Kubernetes
Azure
Architect
Software
Reliability
Data Engineering
Logging
SQL
Distributed Systems
Python
Cloud
API