Platform Engineer - Data & AI

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 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.

Job Summary

We are seeking a highly skilled Platform Engineer - Data & AI to architect and build next-generation AI-native and Agentic platforms that power enterprise-scale data, automation, and intelligent systems.

This role goes beyond traditional data platforms to focus on Agentic AI ecosystems, including multi-agent orchestration, agent lifecycle management, agent communication protocols, and AI-driven platform automation.

You will design and operate a unified platform that supports:

Data pipelines and real-time streaming APIs and microservices GenAI and LLM-powered applications Agentic workflows and multi-agent systems

Working closely with AI/ML engineers, platform teams, SRE, and product teams, you will help build a scalable, observable, and governed AI platform on Google Cloud, leveraging automation, IaC, and modern cloud-native patterns.

Responsibilities

Platform & Cloud Engineering

Architect and build cloud-native platforms on Google Cloud (GCP) supporting data, AI, and agentic workloads Design event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent systems Build scalable microservices and APIs using modern frameworks (e.g., Java, Spring Boot) Develop and manage real-time and batch data pipelines using Airflow, Dataform, Dataflow, Spark, or similar tools Implement Infrastructure-as-Code (IaC) using Terraform and Kubernetes for scalable, repeatable deployments Enable platform automation using CI/CD, GitOps, and self-service frameworks Ensure platform scalability, reliability, and cost efficiency

Agentic Platform & Multi-Agent Systems

Design and build Agentic Platforms that support:

Agent lifecycle management Task orchestration Context and memory handling

Develop and orchestrate multi-agent systems using frameworks such as CrewAI, LangGraph, AutoGen, or equivalent. Implement agent communication and coordination patterns across distributed systems. Build and integrate:

Agent Gateway for managing agent interactions and routing A2A (Agent-to-Agent) communication protocols MCP (Model Context Protocol) or equivalent for context sharing and orchestration ADK (Agent Development Kits) or internal frameworks for rapid agent development

Enable use cases such as:

Autonomous pipeline monitoring and remediation AI-assisted platform operations Intelligent workflow automation Code and data pipeline generation

AI & GenAI Platform Engineering

Integrate LLMs and GenAI services (e.g., OpenAI, Gemini, Claude) into platform workflows. Build and support:

RAG pipelines and retrieval systems Vector search and embedding architectures (Weaviate, Pinecone, FAISS)

Enable AI-driven automation for:

Platform operations Data quality monitoring Incident analysis and resolution

Develop reusable AI platform services and APIs for enterprise consumption.

Agent Observability & AI Operations

Design and implement Agent Observability frameworks, including:

Agent execution tracing Decision tracking and explainability Latency and performance monitoring Failure and retry analysis

Integrate observability using tools like:

OpenTelemetry, Prometheus, Grafana AI/LLM observability tools (e.g., prompt tracing, evaluation frameworks)

Enable end-to-end observability across data pipelines, APIs, and agent workflows.

Data Architecture & Governance

Lead initiatives in:

Data modeling and semantic layer design Data cataloging and metadata management Data quality and lineage tracking

Implement governance frameworks using tools such as DataHub, Collibra, or equivalent. Support data mesh and data fabric architectures for federated data ownership.

Automation & Intelligent Platform Operations

Build automation-first platforms leveraging:

AI-driven workflows Self-healing systems Event-driven automation

Use GenAI to:

Automate operational tasks Generate platform configurations and code Enhance developer productivity

Collaborate with SRE and Production Support teams to improve:

Reliability Incident response Operational efficiency

Engineering Enablement

Develop platform SDKs, CLIs, and reusable blueprints Enable self-service platform capabilities for engineering teams Standardize best practices for:

APIs Data pipelines Agent development

Mentor engineers and promote a culture of innovation and continuous learning

Qualifications

Experience

6-10 years of experience in Platform Engineering, Data Engineering, Cloud Architecture, or AI Platform Engineering Proven experience building enterprise-scale data and AI platforms.

Core Technical Skills

Strong programming expertise in Java, Python, Full-Stack and SQL Experience building microservices and API-driven architectures Deep understanding of distributed systems and cloud-native design

Cloud & Platform Engineering

Strong experience with Google Cloud Platform (GCP) (mandatory) Hands-on experience with:

Kubernetes and containerized workloads Terraform and Infrastructure-as-Code CI/CD pipelines and GitOps

Streaming & Data Systems

Experience with Kafka, Pub/Sub, Spark, Flink, or similar systems. Strong background in real-time and batch data processing.

AI, GenAI & Agentic Systems

Hands-on experience with:

LLM frameworks and APIs Multi-agent orchestration frameworks (CrewAI, LangGraph, AutoGen, etc.) RAG pipelines and vector databases

Experience building or working with:

Agent Gateway architectures A2A communication models MCP or context-sharing frameworks Agent Development Kits (ADKs)

Full Stack & UI…