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.