Staff Software Engineer

About the Role

The Staff Platform Engineer will design, build, and maintain intelligent infrastructure that powers AI, GenAI, and data‑intensive workloads. Working closely with data scientists, ML engineers, software engineers, and product managers, the role delivers a highly scalable platform for data pipelines, APIs, real‑time streaming, and agentic GenAI workflows while supporting federated data architectures. • Develop and maintain real‑time and batch data pipelines using Airflow, dbt, Dataform, and Dataflow/Spark. • Design event‑driven architectures with Apache Kafka, Google Pub/Sub, or equivalent messaging systems. • Build high‑performance data APIs and microservices for downstream applications, ML workflows, and GenAI agents. • Architect and manage multi‑cloud and hybrid cloud platforms (GCP, AWS, Azure) optimized for AI/ML workloads. • Create reusable IaC frameworks with Terraform, Kubernetes, and CI/CD to enable self‑service automation. • Ensure platform scalability, resilience, and cost efficiency via GitOps, observability, and chaos engineering. • Lead data modeling, semantic layer design, and data cataloging to improve data quality and discoverability. • Implement enterprise‑wide data governance, schema enforcement, and lineage tracking using tools like DataHub or Collibra. • Promote data fabric and mesh principles for federated ownership and domain‑driven data product development. • Integrate LLM APIs (OpenAI, Gemini, Claude) into platform workflows for intelligent automation. • Build and orchestrate multi‑agent systems with frameworks such as CrewAI, LangGraph, or AutoGen. • Develop and optimize vector search and RAG pipelines using Weaviate, Pinecone, or FAISS. • Create extensible CLIs, SDKs, and blueprints to simplify onboarding and standardize best practices. • Collaborate across teams to enforce cost, reliability, and security standards within platform blueprints. • Drive technical leadership across AI‑native data platforms, automation systems, and self‑service tools.

Key Responsibilities

• ▸data pipelines
• ▸event streaming
• ▸api development
• ▸cloud architecture
• ▸iac automation
• ▸data governance

What You Bring

The ideal candidate brings a strong background in building and maintaining scalable AI and data platforms, optimizing workflows, and ensuring reliability and performance of data systems. They will also provide technical leadership across AI‑native platforms and automation tools. Qualifications include 5‑8 years of hands‑on experience in platform or data engineering, cloud architecture, and AI engineering, strong programming skills in Java, Python, and SQL, deep knowledge of data modeling, distributed systems, Kubernetes, serverless workloads, and streaming technologies, as well as experience with metadata management, GenAI/LLM frameworks, vector search, observability tools, and ML platforms.

Requirements

• ▸java
• ▸python
• ▸kubernetes
• ▸cloud
• ▸ai platforms
• ▸5‑8 years

Benefits

Equinix offers a high‑energy, mission‑driven environment that embraces innovation, open‑source, and experimentation, along with a commitment to equal employment opportunity, accessibility accommodations, and the use of AI in the hiring process.

Work Environment

Hybrid