Hybrid Principal Platform Engineer – Data & AI

at Equinix

• Job title: Principal Platform Engineer – Data & AI
• Job type: Full Time
• Experience level: Lead
• Salary: Not specified
• Degree requirement: Bachelor's Degree
• Tech skills: Airflow, Apache, AWS, Azure, Cloud, Distributed Systems, Google Cloud Platform, Grafana, Java, Kafka, Kubernetes, Microservices, Prometheus, Python, Spark, SQL, Terraform
• Location requirements: Hybrid, Bangalore, India

About the role

• Principal Platform Engineer at Equinix responsible for architecting, building, and maintaining intelligent infrastructure powering AI and data workloads. Collaborating with cross-functional teams to enhance data management systems.

Responsibilities

• 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
• 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
• 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
• 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

Requirements

• 15 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.

Benefits

• 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.