Senior Platform Engineer

**Senior Platform Engineer - Data & AI**:

- JR-151601
- Hybrid
- Singapore
- Information Technology
- Full time

Who are we?

Equinix is the world’s digital infrastructure company®, operating over 260 data centers across the globe. Digital leaders harness Equinix's trusted platform to bring together and interconnect foundational infrastructure at software speed. Equinix enables organizations to access all the right places, partners and possibilities to scale with agility, speed the launch of digital services, deliver world-class experiences and multiply their value, while supporting their sustainability goals.

Our culture is based on collaboration and the growth and development of our teams. We hire hardworking people who thrive on solving challenging problems and give them opportunities to hone new skills and try new approaches, as we grow our product portfolio with new software and network architecture solutions. We embrace diversity in thought and contribution and are committed to providing an equitable work environment that is foundational to our core values as a company and is vital to our success.

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.

**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
- 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
- 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

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