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Job Summary

We’re looking for a Senior Platform Engineer with a strong foundation in 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

Platform & Cloud Engineering

• Develop and maintain real-time and batch data pipelines using tools like Airflow, dbt, Dataform, and Dataflow/Spark

• Design and develop event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent messaging systems

• Build and expose high-performance data APIs and microservices to support downstream applications, ML workflows, and GenAI agents

• Architect and manage multi-cloud and hybrid cloud platforms (e.g., GCP, AWS, Azure) optimized for AI, ML, and real-time data processing workloads

• Build reusable frameworks and infrastructure-as-code (IaC) using Terraform, Kubernetes, and CI/CD to drive self-service and automation

• Ensure platform scalability, resilience, and cost efficiency through modern practices like GitOps, observability, and chaos engineering

Data Architecture & Governance

• Lead initiatives in data modeling, semantic layer design, and data cataloging, ensuring data quality and discoverability across domains

• Implement enterprise-wide data governance practices, schema enforcement, and lineage tracking using tools like DataHub, Amundsen, or Collibra

• Guide adoption of data fabric and mesh principles for federated ownership, scalable architecture, and domain-driven data product development

AI & GenAI Platform Integration

• Integrate LLM APIs (OpenAI, Gemini, Claude, etc.) into platform workflows for intelligent automation and enhanced user experience

• Build and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, or AutoGen for use cases such as pipeline debugging, code generation, and MLOps

• Experience 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 datasets

Engineering Enablement

• Create extensible CLIs, SDKs, and blueprints to simplify onboarding, accelerate development, and standardize best practices

• Streamline onboarding, documentation, and platform implementation & support using GenAI and conversational interfaces

• Collaborate across teams to enforce cost, reliability, and security standards within platform blueprints

• Act as a thought leader across engineering by introducing platform enhancements, observability, and cost optimization techniques

• Mentor junior engineers and foster a culture of ownership, continuous learning, and innovation

Qualifications

• 8–12 years of hands-on experience in Platform or Data Engineering, Cloud Architecture, AI Engineering roles

• Strong programming background in Java, Python, SQL, and one or more general-purpose languages

• Deep knowledge of data modeling, distributed systems, and API design in production environments

• Proficiency 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 Platform

• Proven experience building scalable, efficient data pipelines for structured and unstructured data

• Experience with GenAI/LLM frameworks and tools for orchestration and workflow automation

• Experience with RAG pipelines, vector databases, and embedding-based search.

• Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) and strong debugging skills across the stack

• Experience with ML Platforms (MLFlow, Vertex AI, Kubeflow) and AI/ML observability tools

• Prior implementation of data mesh or data fabric in a large-scale enterprise

• Experience with Looker Modeler, LookML, or semantic modeling layers

Why You’ll Love This Role

• Drive technical leadership across AI-native data platforms, automation systems, and self-service tools

• Collaborate across teams to shape the next generation of intelligent platforms in the enterprise

• Work with a high-energy, mission-driven team that embraces innovation, open-source, and experimentation

Equinix is committed to ensuring that our employment process is open to all individuals, including those with a disability.  If you are a qualified candidate and need assistance or an accommodation, please let us know by completing this form.

Equinix 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.