Affiliation
Red Hat
Qualifications for candidacy
https://github.com/lfedgeai/AIOps
https://github.com/finos-labs/open-mortgage-data-pipeline
https://github.com/opendatahub-io-contrib/datamesh-platform
Bio
I'm a Red Hat Chief Architect with extensive experience in open-source technologies, cloud-native platforms, data platforms, AI/ML, and enterprise architecture.
Over the years, I have worked with customers across North America and APAC, helping organizations adopt open-source technologies and build modern platforms for data, AI, observability, and cloud-native applications.
I have contributed to and participated in multiple open-source initiatives and communities, including projects related to data platforms, observability, cloud-native technologies, and sustainability initiatives such as OS-Climate.
My areas of focus include:
OpenShift and Kubernetes
OpenShift AI
Data Mesh and Data Lakehouse architectures
Apache Iceberg, Spark, Ray, and Trino
OpenTelemetry and Observability
AI/ML platforms and Agentic AI
Edge Computing and Distributed Systems
Open-source community engagement and co-engineering
I have also worked closely with customers, partners, and open-source communities to drive adoption, contribute ideas, build reference architectures, and help organizations successfully implement open-source solutions at enterprise scale.
What impact do you hope to have on the ecosystem during your tenure as TOC member?
My vision is to help bridge the gap between open-source innovation and real-world enterprise adoption.
Over the years, I have seen many organizations struggle with data silos, fragmented platforms, vendor lock-in, and the complexity of adopting AI at scale. My goal is to help build open, interoperable platforms that enable organizations to own their data, accelerate innovation, and leverage AI responsibly.
Some of the areas I am passionate about include:
Building open Data Mesh and Data Lakehouse architectures
Advancing Sovereign AI and Sovereign Data initiatives
Making AI accessible through open-source technologies
Promoting OpenTelemetry as the standard for observability
Helping organizations adopt cloud-native and AI-native platforms
Enabling partners and communities to build sustainable open-source ecosystems
Creating reusable reference architectures that accelerate customer adoption
Contributing back to open-source communities through collaboration and co-engineering
My long-term goal is to help create open platforms where data, AI, and observability work together seamlessly, while building a strong community of contributors, partners, and customers who can learn from each other and accelerate innovation together.
I strongly believe that the future of AI, data platforms, and cloud infrastructure will be driven by open standards, open communities, and collaborative innovation rather than proprietary silos.
Are there any FINOS Strategic Initiatives you would be interested in working on?
Yes. Based on my background and current focus areas, I would be particularly interested in contributing to and helping champion initiatives around:
Open Data Mesh & Data Platforms
Open architectures for data sharing, governance, and interoperability
Data products, data contracts, and federated governance models
Sovereign AI & Sovereign Data
Open architectures that enable organizations to retain control of their data and AI workloads
Hybrid and multi-cloud AI operating models
Open Source AI & Agentic AI
Reference architectures for AI agents, RAG, MCP, and AI governance
Integration of AI with enterprise data platforms and observability systems
Observability & Operational Intelligence
OpenTelemetry-based architectures for logs, metrics, traces, and events
AI-driven operations (AIOps) and automated remediation
Data Lakehouse Standards
Apache Iceberg adoption and interoperability
Open metadata, lineage, governance, and catalog standards
Open architectures that accelerate adoption across banks, insurance, and capital markets
Reusable blueprints that reduce implementation complexity and vendor lock-in
One area I would particularly like to champion is the convergence of Data Mesh, Sovereign AI, and Open Financial Data Platforms, helping define open reference architectures that enable financial institutions to build scalable, governed, AI-ready data platforms using open-source technologies and open standards.
Confirmation & Commitment
Affiliation
Red Hat
Qualifications for candidacy
https://github.com/lfedgeai/AIOps
https://github.com/finos-labs/open-mortgage-data-pipeline
https://github.com/opendatahub-io-contrib/datamesh-platform
Bio
I'm a Red Hat Chief Architect with extensive experience in open-source technologies, cloud-native platforms, data platforms, AI/ML, and enterprise architecture.
Over the years, I have worked with customers across North America and APAC, helping organizations adopt open-source technologies and build modern platforms for data, AI, observability, and cloud-native applications.
I have contributed to and participated in multiple open-source initiatives and communities, including projects related to data platforms, observability, cloud-native technologies, and sustainability initiatives such as OS-Climate.
My areas of focus include:
OpenShift and Kubernetes
OpenShift AI
Data Mesh and Data Lakehouse architectures
Apache Iceberg, Spark, Ray, and Trino
OpenTelemetry and Observability
AI/ML platforms and Agentic AI
Edge Computing and Distributed Systems
Open-source community engagement and co-engineering
I have also worked closely with customers, partners, and open-source communities to drive adoption, contribute ideas, build reference architectures, and help organizations successfully implement open-source solutions at enterprise scale.
What impact do you hope to have on the ecosystem during your tenure as TOC member?
My vision is to help bridge the gap between open-source innovation and real-world enterprise adoption.
Over the years, I have seen many organizations struggle with data silos, fragmented platforms, vendor lock-in, and the complexity of adopting AI at scale. My goal is to help build open, interoperable platforms that enable organizations to own their data, accelerate innovation, and leverage AI responsibly.
Some of the areas I am passionate about include:
Building open Data Mesh and Data Lakehouse architectures
Advancing Sovereign AI and Sovereign Data initiatives
Making AI accessible through open-source technologies
Promoting OpenTelemetry as the standard for observability
Helping organizations adopt cloud-native and AI-native platforms
Enabling partners and communities to build sustainable open-source ecosystems
Creating reusable reference architectures that accelerate customer adoption
Contributing back to open-source communities through collaboration and co-engineering
My long-term goal is to help create open platforms where data, AI, and observability work together seamlessly, while building a strong community of contributors, partners, and customers who can learn from each other and accelerate innovation together.
I strongly believe that the future of AI, data platforms, and cloud infrastructure will be driven by open standards, open communities, and collaborative innovation rather than proprietary silos.
Are there any FINOS Strategic Initiatives you would be interested in working on?
Yes. Based on my background and current focus areas, I would be particularly interested in contributing to and helping champion initiatives around:
Open Data Mesh & Data Platforms
Open architectures for data sharing, governance, and interoperability
Data products, data contracts, and federated governance models
Sovereign AI & Sovereign Data
Open architectures that enable organizations to retain control of their data and AI workloads
Hybrid and multi-cloud AI operating models
Open Source AI & Agentic AI
Reference architectures for AI agents, RAG, MCP, and AI governance
Integration of AI with enterprise data platforms and observability systems
Observability & Operational Intelligence
OpenTelemetry-based architectures for logs, metrics, traces, and events
AI-driven operations (AIOps) and automated remediation
Data Lakehouse Standards
Apache Iceberg adoption and interoperability
Open metadata, lineage, governance, and catalog standards
Open architectures that accelerate adoption across banks, insurance, and capital markets
Reusable blueprints that reduce implementation complexity and vendor lock-in
One area I would particularly like to champion is the convergence of Data Mesh, Sovereign AI, and Open Financial Data Platforms, helping define open reference architectures that enable financial institutions to build scalable, governed, AI-ready data platforms using open-source technologies and open standards.
Confirmation & Commitment