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

Responsibilities:

• Architect and build cloud-native platforms on Google Cloud supporting data, AI, and agentic workloads
• Design event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent
• Build scalable microservices and APIs using modern frameworks
• Develop and manage real-time and batch data pipelines
• Implement Infrastructure-as-Code using Terraform and Kubernetes
• Enable platform automation using CI/CD, GitOps, and self-service frameworks
• Design and build Agentic Platforms supporting agent lifecycle management, task orchestration, context and memory handling
• Develop and orchestrate multi-agent systems using frameworks such as CrewAI, LangGraph, AutoGen
• Implement agent communication and coordination patterns
• Build and integrate Agent Gateway, A2A communication protocols, MCP, and ADKs
• Integrate LLMs and GenAI services into platform workflows
• Build and support RAG pipelines and vector search architectures
• Enable AI-driven automation for platform operations and data quality monitoring
• Design and implement Agent Observability frameworks including execution tracing, decision tracking, and performance monitoring
• Lead data modeling, semantic layer design, data cataloging, metadata management, and governance frameworks
• Build automation-first platforms leveraging AI-driven workflows and self-healing systems
• Collaborate with SRE and Production Support teams to improve reliability and operational efficiency
• Develop platform SDKs, CLIs, and reusable blueprints
• Enable self-service platform capabilities for engineering teams
• Standardize best practices for APIs, data pipelines, and agent development
• Mentor engineers and promote a culture of innovation and continuous learning

Additional responsibilities:

• Ensure platform scalability, reliability, and cost efficiency
• Support data mesh and data fabric architectures for federated data ownership
• Use GenAI to automate operational tasks and generate platform configurations and code
• Build platform dashboards, developer portals, and observability UIs

Nice to have:

• Experience with Vertex AI, MLflow, Kubeflow, or ML platforms
• Prior implementation of data mesh or data fabric architectures
• Experience with Looker Modeler / LookML or semantic layers
• Exposure to AI safety, governance, and responsible AI practices
• Experience building enterprise AI/Agentic platforms at scale

More skills:

Java, Python, Full-Stack, SQL, Google Cloud Platform (GCP), Kubernetes, Terraform, CI/CD, GitOps, Apache Kafka, Google Pub/Sub, Spark, Flink, Airflow, Dataform, Dataflow, Spring Boot, FAISS, OpenTelemetry, Prometheus, Grafana, DataHub, Collibra, React, Angular, Vue.js, REST APIs, GraphQL, WebSockets, Looker Modeler, LookML

Other:

• Work on cutting-edge Agentic AI and multi-agent systems
• Drive innovation in automation, GenAI, and intelligent systems
• Collaborate with high-impact teams across data, AI, and platform engineering
• Shape the future of AI-driven enterprise architecture
• Equinix is committed to equal employment opportunity and accommodation for disabilities
• Use of artificial intelligence in the hiring process