- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
- AWS
- Python
- GCP
- NLP
- Typescript
- Terraform
- Ci/Cd
- Leadership
- Docker
- Github Actions
- Azure
- Communication
- Machine Learning
- Kubernetes
- Architected generative text pipeline serving 10M daily API calls with 99.95% uptime.
- Implemented observability stack reducing incident MTTR from 8 hours to 1 hour.
- Migrated model training workflows from on-premise to AWS, cutting compute costs by 40%.
- Improved test coverage across NLP services from 70% to 92% with policy-driven automation.
- Coached cross-functional teams on AI ethics best practices for customer-facing features.
