Backend & AI Platform Engineer focused on building production-ready APIs, microservices, LLM applications, RAG systems, data infrastructure and developer tooling.
I work across Python, FastAPI, PHP/Laravel, Rust, Docker, PostgreSQL, Redis, AWS/GCP, CI/CD, LLM integrations, vector search and modern AI engineering workflows.
Currently building AuraDB and Aura Connector, two open-source infrastructure projects designed around AI-native applications, typed data access, vector search and backend reliability.
I design and build backend systems, AI-powered applications and cloud-ready software architectures.
My main areas of focus are:
- Backend Engineering — REST APIs, microservices, authentication, data models, async services and production-grade API design.
- AI / LLM Engineering — RAG pipelines, LLM integrations, document generation, embeddings, vector search and AI workflow orchestration.
- DevOps & Cloud — Dockerized environments, CI/CD pipelines, cloud services, deployment workflows and observability.
- Data Infrastructure — PostgreSQL, Redis, vector stores, ETL pipelines, data processing and database-oriented tooling.
- Full Stack Delivery — Laravel, React, JavaScript/TypeScript, admin platforms, internal tools and enterprise applications.
AuraDB is a Rust-native multi-model database server designed for modern AI-native applications.
It explores a unified data layer for structured records, documents, relationships and vector search, with a focus on backend reliability, developer ergonomics and production-oriented architecture.
Highlights
- Rust-native database server
- Multi-model data architecture
- Persistent storage
- MVCC snapshot isolation
- Query planning
- Vector search support
- Authentication and TLS
- Docker-based deployment
- CLI tooling and observability
Repository: github.com/Ohswedd/auradb
Aura Connector is an async, type-safe Python data connector designed to provide a clean developer experience across AuraDB and other database backends.
It focuses on typed models, injection-safe query building, vector fields, migrations, observability and extensible backend support.
Highlights
- Async Python API
- Type-safe models
- Injection-safe query builder
- Vector field support
- Migration system
- Observability hooks
- Multi-backend design
- Built for AI-native applications
Repository: github.com/Ohswedd/aura-connector
SentinelQA is a release-confidence and quality-gate engine for modern web applications.
It is designed to help teams evaluate application quality through reproducible checks, automated testing workflows and CI-friendly reports.
Focus areas
- Playwright-based audits
- CI/CD quality gates
- Security-oriented checks
- Accessibility checks
- Release confidence scoring
- Developer tooling for AI-built and human-built applications
Repository: github.com/Ohswedd/sentinelqa
I have 5+ years of experience building software for enterprise, public-sector and private-sector environments.
My work includes:
- Backend systems for public administration platforms
- REST APIs and microservices for enterprise clients
- Cloud ETL pipelines on AWS and GCP
- AI-assisted document generation systems
- RAG pipelines and LLM-based applications
- CI/CD workflows and Dockerized deployment environments
- Technical documentation, architecture diagrams and delivery coordination
- Full stack platforms using Python, PHP/Laravel, JavaScript, React and WordPress ecosystems
I am currently focused on:
- Building AI-native backend infrastructure
- Improving AuraDB and Aura Connector
- Designing better abstractions for vector search and typed data access
- Exploring LLM application architectures
- Building production-ready APIs and microservices
- Strengthening DevOps, CI/CD and observability practices
I care about software that is:
- Clear in architecture
- Easy to deploy
- Well documented
- Observable
- Secure by design
- Tested where it matters
- Useful for real developers and real users
My approach combines product thinking, backend engineering, AI experimentation and production-readiness.
I am interested in roles related to:
- AI Engineer
- LLM Engineer
- Backend Engineer
- DevOps Engineer
- MLOps Engineer
- AI Platform Engineer
- Machine Learning Engineer
- Data Platform Engineer
- Developer Tooling Engineer
Especially where backend systems, cloud infrastructure, AI applications and production engineering meet.
Building backend and AI infrastructure with a focus on reliability, developer experience and real-world usability.