Platform Engineer
Equinix
Design, build, and maintain scalable AI/Data platform on cloud for ML/GenAI workloads.
8 days ago ago
Intermediate (4-7 years), Expert & Leadership (13+ years), Experienced (8-12 years)
Full Time
Singapore
Hybrid
Company Size
10,000 Employees
Service Specialisms
Data center colocation
Interconnection services
Smart Hands / remote support
Software-defined interconnection
Network Edge services
Equinix Metal (bare metal)
Equinix Fabric (cloud routing)
Managed services (integration & advisory)
Sector Specialisms
No specialisms available
Role
What you would be doing
multi‑cloud
data pipelines
event‑driven
apis
iac
observability
- Architect and manage multi-cloud and hybrid cloud platforms (e.g., GCP, AWS, Azure) optimized for AI, ML, and real-time data processing workloads
- Lead initiatives in data modeling, semantic layer design, and data cataloging, ensuring data quality and discoverability across domains
- Guide adoption of data fabric and mesh principles for federated ownership, scalable architecture, and domain-driven data product development
- Build and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, or AutoGen for use cases such as pipeline debugging, code generation, and MLOps
- 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
- Create extensible CLIs, SDKs, and blueprints to simplify onboarding, accelerate development, and standardize best practices
- Work with engineering by introducing platform enhancements, observability, and cost optimization techniques
- Build and expose high-performance data APIs and microservices to support downstream applications, ML workflows, and GenAI agents
- Implement enterprise-wide data governance practices, schema enforcement, and lineage tracking using tools like DataHub, Amundsen, or Collibra
- Build reusable frameworks and infrastructure-as-code (IaC) using Terraform, Kubernetes, and CI/CD to drive self-service and automation
- Integrate LLM APIs (OpenAI, Gemini, Claude, etc.) into platform workflows for intelligent automation and enhanced user experience
- Foster a culture of ownership, continuous learning, and innovation
- Ensure platform scalability, resilience, and cost efficiency through modern practices like GitOps, observability, and chaos engineering
- 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
- Collaborate across teams to shape the next generation of intelligent platforms in the enterprise
- Drive technical leadership across AI-native data platforms, automation systems, and self-service tools
What you bring
genai
kubernetes
prometheus
java
mlflow
vector db
- 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)
- Proficiency in designing and managing Kubernetes, serverless workloads, and streaming systems (Kafka, Pub/Sub, Flink, Spark)
- Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) and strong debugging skills across the stack
- 5+ years of hands-on experience in Platform or Data Engineering, Cloud Architecture, AI Engineering roles
- Experience with GenAI/LLM frameworks and tools for orchestration and workflow automation
- Strong programming background in Java, Python, SQL, and one or more general-purpose languages
- 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 ML Platforms (MLFlow, Vertex AI, Kubeflow) and AI/ML observability tools
- 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
- Prior implementation of data mesh or data fabric in a large-scale enterprise
- Experience with Looker Modeler, LookML, or semantic modeling layers
- Deep knowledge of data modeling, distributed systems, and API design in production environments
- Experience with RAG pipelines, vector databases, and embedding-based search
- Work with a high-energy, mission-driven team that embraces innovation, open-source, and experimentation
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Interview process
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Security clearanceInformation not given or found
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Company
Overview
1998
Year Established
The company was established in 1998, marking the beginning of its journey in providing data center and interconnection services.
25+ Countries
Global Presence
Operates in over 25 countries, showcasing its extensive international reach and capability to serve a global clientele.
200+ Data Centers
Global Infrastructure
Provides essential services through over 200 data centers worldwide, supporting digital ecosystems and business growth.
- World’s largest provider of data center and interconnection services.
- Offers cutting-edge solutions that enable businesses to scale and adapt to the digital age.
- Facilitates high-performance connections for industries like cloud computing, telecommunications, and finance.
- Provides services including hybrid cloud solutions, network and application performance optimization, and interconnection for business ecosystems.
- At the forefront of advancing global digital transformation through strategic partnerships with leading companies.
- Innovative business models drive enterprise infrastructure modernization, ensuring security and compliance.
- Thriving workplace where every colleague is valued and respected for who they are and what they contribute
- Foster belonging—cultivating an environment where every employee feels empowered to bring their authentic selves to work
- Deliberate inclusion of all perspectives to strengthen decision‑making and business outcomes
- Programs to enhance workplace experience and attract high‑performing talent
Net-zero by 2040
Greenhouse Gas Emissions Target
Committed to achieving net-zero greenhouse gas emissions across all scopes by 2040, verified by Science Based Targets.
50% reduction by 2030
Emission Reduction Target
Reducing Scope 1/2 emissions and fuel/energy-related Scope 3 emissions by half from 2019 levels by 2030.
100% Renewable Energy
Global Portfolio Goal
Aiming to source 100% of energy from renewable sources across all operations by 2030, having reached 96% in 2024.
A-List Recognition
CDP Climate Change
Achieved consecutive years of 'A-List' recognition from CDP for climate change management and transparency.
- Target global average PUE of 1.33 by 2030; achieved 1.39 in latest reporting (6% improvement YoY)
- Installed 72 MW fuel cells (capable of Hâ‚‚ blends), avoiding 285,000 MTCOâ‚‚e and 382 billion gallons of embedded water use
- Consumed 8,560 GWh electricity across 268 data centers in 35 countries, with 1,285 MW of long-term PPAs
- Reduced operational Scope 1/2 emissions 24% from 2019 baseline; total 2024 footprint 1,747,700 MTCOâ‚‚e
- Issued ~$6.9 billion in green bonds to finance green buildings, energy efficiency, renewable energy, and decarbonization projects
17% increase
Women Employees Increase
Represents a significant global growth in the number of women employees over the past year.
14% increase
Black/African-American Employees
Signifies a notable rise in the number of U.S.-based Black/African-American employees.
47 organizations
Digital Inclusion Funding
Reflects the number of organizations supported by the foundation to enhance digital access and skills development.
11–37% increase
Employee Volunteer Hours
Highlights the growth in global volunteer hours, with a notable example of 25,300 hours in the latest reporting period.
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