Senior Platform Engineer, Equinix

• 8–12 years of experience in Platform Engineering, Data Engineering, Cloud Architecture, or AI Platform Engineering
• Proven experience building enterprise-scale data and AI platforms
• 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
• 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
• Experience with Kafka, Pub/Sub, Spark, Flink, or similar systems
• Strong background in real-time and batch data processing
• 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)
• Experience building full stack applications with modern frontend frameworks (React, Angular, Vue.js)
• Strong understanding of REST/GraphQL APIs and UI integration patterns
• Experience with real-time UI updates using WebSockets or streaming architectures
• Familiarity with design systems, UX principles, and responsive design
• Experience building platform dashboards, developer portals, or observability UIs
• Experience with observability tools:
• Prometheus, Grafana, OpenTelemetry
• Strong debugging and system analysis skills
• Familiarity with AI/LLM observability and evaluation frameworks
• Experience with:
• Data catalogs and metadata platforms
• Data quality and lineage frameworks
• Semantic modeling and data governance

Desirable

• We are seeking a highly skilled Senior 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
• 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
• Work on cutting-edge Agentic AI and multi-agent systems
• Build AI-native enterprise platforms at scale
• 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