Software Engineer

Traditional portfolio websites prioritize presentation but overlook content management. Experiences, projects, and case studies quickly become scattered across pages and difficult to update consistently. I needed a way to structure my professional journey so that information could be reused, transformed into different narratives, and published without repeatedly editing source code.

Designed the portfolio around structured entities such as experiences, projects, skills, and case studies instead of hardcoded pages. Built a dedicated admin dashboard for managing all portfolio content. Developed reusable content models that could power multiple public-facing views. Introduced relationships between experiences, projects, and case studies to provide richer storytelling. Implemented AI-assisted workflows to generate insights, identify presentation gaps, and synthesize career narratives while keeping human review in the loop. Created a modern public experience optimized for maintainability and future growth. Established deployment workflows to continuously evolve the platform as both a product and a personal knowledge base.

The project evolved beyond a personal website into an engineering knowledge platform capable of documenting experiences, generating richer narratives, and supporting multiple perspectives of the same underlying data.

Content updates became faster and more consistent, new features could be introduced without restructuring the entire application, and AI-assisted experiences became possible through the use of structured information rather than fragmented content.

Running local LLMs worked well when my workstation was available, but the approach quickly exposed operational limitations:

• Long-running AI tasks blocked the application flow.
• Heavy local inference wasn't always practical while actively using my machine.
• The system became unavailable whenever my computer was offline.
• Failures were difficult to monitor and retry consistently.
• Some workflows required asynchronous processing rather than immediate responses.

What started as a personal productivity tool gradually evolved into a distributed task-processing problem.

I designed the platform using a hybrid execution model.

Local-first AI execution

• Local LLMs handled most day-to-day CV analysis and job matching workflows.
• Personal data remained on my own hardware whenever possible.
• The architecture avoided unnecessary dependence on third-party AI APIs.
• Queue-based task orchestration

As workloads became longer and more expensive to execute synchronously, I introduced asynchronous processing:

• AI requests were converted into jobs.
• Jobs were pushed into Amazon SQS queues.
• Priority-based execution separated urgent and background workloads.
• Dead-letter queues captured failed tasks for investigation.
• Workers consumed queued tasks independently from the user-facing application.
• Task states were tracked throughout their lifecycle. Cloud deployment

To make the system available beyond my local environment:

• Containerized services were deployed to AWS.
• Amazon ECS handled worker execution.
• Amazon ECR managed container images.
• Amazon SQS coordinated distributed processing.
• PostgreSQL stored application and task metadata.
• Infrastructure was provisioned using Terraform.

The project evolved from a personal CV assistant into a resilient AI processing platform capable of balancing privacy, cost, and reliability.

More importantly, it changed how I think about AI systems: LLM integration is rarely just a prompt engineering problem. In practice, it becomes an exercise in task orchestration, operational resilience, and designing around the realities of expensive and unpredictable workloads.

The challenge was determining how to leverage AI responsibly to increase productivity without compromising engineering judgment.

• Integrated GitHub Copilot, Claude, and Codex into development workflows.
• Used AI to accelerate implementation and exploration.
• Built LLM-powered applications, including a CV and ATS match analyzer.
• Added AI insights capabilities into my portfolio administration experience.
• Experimented with local and hosted LLM approaches.

• Increased development efficiency.
• Expanded experimentation capacity.
• Delivered practical AI-enabled experiences.
• Strengthened my ability to combine traditional engineering with emerging technologies.