I am transitioning into AI Automation Engineering, building practical workflows that connect AI models with business processes and everyday tools.
My background in talent acquisition, operations, sales development, and data analysis helps me approach automation from both sides: understanding the real workflow first, then designing a clear and useful technical solution.
I am currently learning and building with:
n8n · OpenAI API · AI Agents · RAG · Embeddings · Vector Databases · Pinecone · Structured Outputs · Gmail Automation · Google Sheets · Workflow Testing
My focus is on:
- Designing end-to-end AI automation workflows
- Connecting AI models with business tools and data sources
- Building knowledge assistants with retrieval-augmented generation
- Creating structured, testable, and maintainable automations
- Applying automation to recruitment, operations, and customer communication
An n8n and OpenAI RAG prototype for retrieving answers from a controlled recruitment-policy knowledge base. It uses document ingestion, recursive text splitting, embeddings, Pinecone vector storage, and AI-agent retrieval.
This project connects my Talent Acquisition experience with my transition into AI Automation Engineering.
An n8n workflow that receives Gmail messages, uses OpenAI to extract structured information, logs the results in Google Sheets, decides whether a reply is required, generates a response, and sends it through Gmail.
The workflow demonstrates AI analysis, structured outputs, conditional routing, application integrations, and workflow testing.
I learn by building real portfolio projects and documenting the process clearly:
learn → build → test → document → improve
I am currently developing stronger skills in retrieval quality, grounded AI responses, workflow reliability, error handling, evaluation, and production-ready automation design.