Principal Platform Engineer - Data & AI at Equinix in Singapore | Kablio

Role

Description

llm integration

data apis

event-driven

iac

data pipelines

cloud architecture

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
Integrate LLM APIs (OpenAI, Gemini, Claude, etc.) into platform workflows for intelligent automation and enhanced user experience
Guide adoption of data fabric and mesh principles for federated ownership, scalable architecture, and domain-driven data product development
Lead initiatives in data modeling, semantic layer design, and data cataloging, ensuring data quality and discoverability across domains
Build and expose high-performance data APIs and microservices to support downstream applications, ML workflows, and GenAI agents
Design and develop event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent messaging systems
Streamline onboarding, documentation, and platform implementation & support using GenAI and conversational interfaces
Build reusable frameworks and infrastructure-as-code (IaC) using Terraform, Kubernetes, and CI/CD to drive self-service and automation
Develop and maintain real-time and batch data pipelines using tools like Airflow, dbt, Dataform, and Dataflow/Spark
Mentor junior engineers and foster a culture of ownership, continuous learning, and innovation
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 and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, or AutoGen for use cases such as pipeline debugging, code generation, and MLOps
Implement enterprise-wide data governance practices, schema enforcement, and lineage tracking using tools like DataHub, Amundsen, or Collibra
Act as a thought leader across engineering by introducing platform enhancements, observability, and cost optimization techniques
Create extensible CLIs, SDKs, and blueprints to simplify onboarding, accelerate development, and standardize best practices
Collaborate across teams to shape the next generation of intelligent platforms in the enterprise
Ensure platform scalability, resilience, and cost efficiency through modern practices like GitOps, observability, and chaos engineering

Requirements

data engineering

kubernetes

kafka

vector search

genai

lookml

15 years of hands-on experience in Platform or Data Engineering, Cloud Architecture, AI Engineering roles
Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) and strong debugging skills across the stack
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
Experience with GenAI/LLM frameworks and tools for orchestration and workflow automation
Proven experience building scalable, efficient data pipelines for structured and unstructured data
Proficiency in designing and managing Kubernetes, serverless workloads, and streaming systems (Kafka, Pub/Sub, Flink, Spark)
Prior implementation of data mesh or data fabric in a large-scale enterprise
Deep knowledge of data modeling, distributed systems, and API design in production environments
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)
Experience with Looker Modeler, LookML, or semantic modeling layers
Experience with metadata management, data catalogs, data quality enforcement, and semantic modeling & automated integration with Data Platform
Strong programming background in Java, Python, SQL, and one or more general-purpose languages
Experience with RAG pipelines, vector databases, and embedding-based search.
Experience with ML Platforms (MLFlow, Vertex AI, Kubeflow) and AI/ML observability tools

Benefits

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

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found

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.

Culture + Values

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

Environment + Sustainability

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 H₂ blends), avoiding 285,000 MTCO₂e 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 MTCO₂e
Issued ~$6.9 billion in green bonds to finance green buildings, energy efficiency, renewable energy, and decarbonization projects

Inclusion & Diversity

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.

Articles

Investor Relations – Contact & Info

Life at Equinix (Careers Blog)

Customer-Side Support & Feedback

Role

Description

llm integration

data apis

event-driven

iac

data pipelines

cloud architecture

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
Integrate LLM APIs (OpenAI, Gemini, Claude, etc.) into platform workflows for intelligent automation and enhanced user experience
Guide adoption of data fabric and mesh principles for federated ownership, scalable architecture, and domain-driven data product development
Lead initiatives in data modeling, semantic layer design, and data cataloging, ensuring data quality and discoverability across domains
Build and expose high-performance data APIs and microservices to support downstream applications, ML workflows, and GenAI agents
Design and develop event-driven architectures using Apache Kafka, Google Pub/Sub, or equivalent messaging systems
Streamline onboarding, documentation, and platform implementation & support using GenAI and conversational interfaces
Build reusable frameworks and infrastructure-as-code (IaC) using Terraform, Kubernetes, and CI/CD to drive self-service and automation
Develop and maintain real-time and batch data pipelines using tools like Airflow, dbt, Dataform, and Dataflow/Spark
Mentor junior engineers and foster a culture of ownership, continuous learning, and innovation
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 and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, or AutoGen for use cases such as pipeline debugging, code generation, and MLOps
Implement enterprise-wide data governance practices, schema enforcement, and lineage tracking using tools like DataHub, Amundsen, or Collibra
Act as a thought leader across engineering by introducing platform enhancements, observability, and cost optimization techniques
Create extensible CLIs, SDKs, and blueprints to simplify onboarding, accelerate development, and standardize best practices
Collaborate across teams to shape the next generation of intelligent platforms in the enterprise
Ensure platform scalability, resilience, and cost efficiency through modern practices like GitOps, observability, and chaos engineering

Requirements

data engineering

kubernetes

kafka

vector search

genai

lookml

15 years of hands-on experience in Platform or Data Engineering, Cloud Architecture, AI Engineering roles
Familiarity with observability tools (Prometheus, Grafana, OpenTelemetry) and strong debugging skills across the stack
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
Experience with GenAI/LLM frameworks and tools for orchestration and workflow automation
Proven experience building scalable, efficient data pipelines for structured and unstructured data
Proficiency in designing and managing Kubernetes, serverless workloads, and streaming systems (Kafka, Pub/Sub, Flink, Spark)
Prior implementation of data mesh or data fabric in a large-scale enterprise
Deep knowledge of data modeling, distributed systems, and API design in production environments
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)
Experience with Looker Modeler, LookML, or semantic modeling layers
Experience with metadata management, data catalogs, data quality enforcement, and semantic modeling & automated integration with Data Platform
Strong programming background in Java, Python, SQL, and one or more general-purpose languages
Experience with RAG pipelines, vector databases, and embedding-based search.
Experience with ML Platforms (MLFlow, Vertex AI, Kubeflow) and AI/ML observability tools

Benefits

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

Training + Development

Information not given or found

Interview process

Information not given or found

Visa Sponsorship

Information not given or found

Security clearance

Information not given or found

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.

Culture + Values

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

Environment + Sustainability

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 H₂ blends), avoiding 285,000 MTCO₂e 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 MTCO₂e
Issued ~$6.9 billion in green bonds to finance green buildings, energy efficiency, renewable energy, and decarbonization projects

Inclusion & Diversity

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.

Articles

Investor Relations – Contact & Info

Life at Equinix (Careers Blog)

Customer-Side Support & Feedback