I build backend systems, AI-powered applications, cloud infrastructure, and developer tooling — with a bias toward systems that are useful in the real world.
Bernard Karaba
What I build across
Bernard works across the stack where the problem demands it — from application logic and data flows to AI tooling and cloud-native operations.
Designing intelligent systems that connect models, tools, data and workflows.
Systems where AI can use tools, call APIs and work inside explicit constraints instead of isolated demos.
APIs, services, databases, integrations and reliable application architecture.
Strong contracts, validation, data integrity and failure handling sit at the center of reliable software.
Containers, Kubernetes, Linux, CI/CD, cloud infrastructure and observability.
Application engineers need a clear view from git to runtime to metrics, and from code to infrastructure.
Workflow automation, APIs, Power Platform, RPA and system integrations.
Repeating operational work is a design problem, and the right automation makes the system easier to trust.
Building, contributing to and learning through public software.
Learning in public and shipping useful tools gives back more than code — it creates feedback loops.
Selected proof
A few examples of projects and contributions across backend, AI and automation systems.
Transcription and OCR workflows need fast local data access without losing reliability.
Contributed to architecture and SQLite performance work for transcription and OCR datasets, including schema evolution and query optimization patterns.
AI agents need verifiable toolchains and clearer developer workflows.
Contributed to an SDK and tooling layer for verifiable AI agents on Flare using Confidential Space.
A workflow needed a clean connector for external API data inside Microsoft automation tools.
Designed and published a custom API connector through Microsoft’s Independent Publisher program, enabling Power Automate and related workflows.
Credit dispute workflows need secure backend processing with structured data and document-aware automation.
Worked on a backend service for a credit dispute platform, handling sensitive data and API workflows in a compliance-conscious environment.
How I work
01 Understand
Clarify the problem and constraints.
02 Model
Design the system before adding complexity.
03 Build
Create the smallest useful implementation.
04 Verify
Test behavior, failure modes and assumptions.
05 Operate
Measure, observe and improve the system.
Engineering signals
Reliable systems come from the same principles across backend, AI and infrastructure work.
Design for failure, retries, idempotency and observability.
Protect data, credentials and system boundaries.
Measure bottlenecks before optimizing.
APIs, connectors, protocols and integrations.
Turn repeatable operations into reliable workflows.
Test systems against explicit expected behavior.
Talk to Bernard
Have an architecture question, an AI idea, or a system that needs simplifying?