Career Journey
From software engineering and full-stack development to UX, product management, AI solutions and agentic systems.
From software to intelligent systems
Section titled “From software to intelligent systems”My career has evolved across several disciplines of technology — from software engineering and full-stack development, through UX, product design, Agile and product management, to my current focus on AI and agentic systems.
I see these experiences as layers of the same capability:
Understand the problem → design the solution → build the technology → deliver the product → create value.
AI and agentic systems are the next extension of that journey.
The evolution
Section titled “The evolution”Software Engineering
I began by learning how technology is actually built — developing software, working across the stack and understanding the foundations behind digital products.
UX & Product Design
I moved closer to the people using technology — understanding user needs, designing experiences and translating problems into useful products.
Agile & Product Management
I expanded from designing and building solutions to leading their delivery — working with stakeholders, managing priorities and connecting technology with business objectives.
AI Solutions
My focus increasingly shifted toward identifying where AI can solve meaningful business problems, create new capabilities and improve existing products and workflows.
Agentic Systems
Today I build systems where AI agents reason, use tools, coordinate tasks and operate within defined workflows — with humans supervising and guiding the system by design.
What each chapter taught me
Section titled “What each chapter taught me”How technology works
Building software gave me a practical understanding of architecture, implementation, APIs, integration and the realities of delivering technology.
Why people use technology
UX taught me to start with people rather than technology — understanding needs, friction, behavior and the experience surrounding a product.
What is worth building
Product management added prioritization, discovery and value thinking: not every problem needs a solution, and not every solution creates value.
How teams deliver
Agile and Scrum taught me how multidisciplinary teams turn uncertainty into incremental, measurable progress.
Where intelligence adds value
Building with LLMs taught me that the model is rarely the bottleneck — the bottleneck is getting the right information into the context at the right time. Prompt engineering and evaluation methodology matter more than model selection.
How systems can act
Building agents taught me that failure modes live in handoffs and memory, not in the model. An agent’s tools, deduplication strategy and human-in-the-loop boundaries are the surfaces that need deliberate design — see how I applied this in JobScout.
The capability stack
Section titled “The capability stack”My experience can be viewed as a stack of complementary capabilities rather than separate disciplines.
What I do today
Section titled “What I do today”Today, I operate across the boundaries of business, product, design, technology and AI.
My approach to an AI opportunity typically looks like this:
Understand the business problem and opportunity.
Determine whether AI can create meaningful value.
Shape the product, workflow and human-AI interaction.
Develop the AI solution, agents and integrations.
Coordinate agents, tools, data and workflows.
Evaluate behavior, monitor outcomes and improve the system.
Connect the solution back to business and user value.
The intersection I bring
Section titled “The intersection I bring”Architecture
AI
Discovery
Value
Design
Human-AI
Opportunities
Outcomes
The common thread is connecting these disciplines to build useful technology.
Where I am going
Section titled “Where I am going”I am looking for AI Solution Engineer roles where I can build and ship LLM-powered systems — agentic workflows, RAG pipelines, AI products — on a product or platform team.
I bring three things that most candidates at this level don’t have together:
- Production engineering depth — I build, test (165+ tests) and instrument (Langfuse telemetry) real systems, not just demos
- UX and human-AI interaction thinking — I design human-in-the-loop boundaries deliberately, not as an afterthought
- Business framing — I start from the problem, not the technology, and I connect solutions back to measurable value
I want to work on AI that moves beyond experimentation and becomes useful, measurable and trustworthy technology.
How I am learning
Section titled “How I am learning”I am upskilling through a parallel build-write-portfolio approach: I define a project, implement it end-to-end, write about the approach and findings, and add everything to my portfolio. Every project includes an evaluation step — because a system that hasn’t been measured is a prototype, not a solution.
Currently working through Claude Academy courses and building agentic systems with the Claude Agent SDK, ChromaDB and Ollama.
Explore the work
Section titled “Explore the work”The projects and case studies on this site show how I am applying this experience to real problems.