Results for “orpo”

Enforce organizational security policies across Kubernetes clusters and CI/CD pipelines using Open Policy Agent (OPA) and Gatekeeper, including writing Rego policies, deploying admission controllers, and testing policies locally.

Defines a gated Research-Plan-TDD-Review-Commit pipeline for orchestrating feature development, defect fixes, and refactoring tasks through composable phases and human approval gates.

Manage and inspect OPA policies, Gatekeeper constraints, and Rego rules, including discovery, testing, and audit analysis.

Configure Okta as a SAML 2.0 Identity Provider and implement SP-initiated and IdP-initiated SSO flows with attribute mapping, assertion encryption, and security hardening.

Enforce Kubernetes admission policies using OPA Gatekeeper with ConstraintTemplates, Rego rules, and the Gatekeeper policy library.

Orquesta el ecosistema de agentes: decide qué modelo o tier usar, delega tareas a sub-agentes especializados y planifica arquitectura técnica.

Domine SQL moderno com bancos de dados cloud-native, otimização OLTP/OLAP e técnicas avançadas de query. Especialista em tuning de performance, modelagem de dados e sistemas analíticos híbridos.

Expert guidance for OPA (Open Policy Agent), the CNCF policy engine for unified authorization across the stack. Helps developers write Rego policies for Kubernetes admission control, API authorization, infrastructure-as-code validation, and data filtering — enforcing security policies as code.

Local text-to-speech via sherpa-onnx (offline, no cloud)

Query external data from Oxla — Kafka topics via catalogs, Apache Iceberg tables, and S3/GCS/Azure parquet/ORC files — alongside native Oxla tables. Use when querying Kafka topics with CREATE KAFKA CATALOG or CREATE REDPANDA CATALOG, reading Apache Iceberg tables with the catalog=>path.table syntax, loading or.

Master modern SQL with cloud-native databases, OLTP/OLAP optimization, and advanced query techniques. Expert in performance tuning, data modeling, and hybrid analytical systems.

Run autonomous optimization loops that iteratively improve artifacts against evaluators using hypothesis tree refinement, without overfitting.