[01] | PORTFOLIO — 2026
AGENT_EVALS · PASS
Engineering AI systems that act. reason. assist.
Engineering
AI systems
that
act.
reason.
assist.
I’m Prince, an AI Engineer building intelligent systems and automation to solve real human problems. I’m particularly interested in building AI agents that can reason, take action, and be trusted in production.
AGENT_EVALS · PASS
[02] | ABOUT
Prince N. Oboe-Sam
AI Engineer · Agentic Systems
I started my career in Digital Marketing, but my curiosity gradually pulled me deeper into AI. What started as an interest in marketing AI automations has grown into a serious focus on building AI systems.
Today, I’m focused on building agentic AI systems that connect LLMs, data, and business workflows to solve real problems. I enjoy exploring how AI can move from simple automation to systems that can reason, interact with external services, and perform useful tasks.
I’m particularly interested in building reliable AI agents that go beyond generating responses to understanding context, taking action, and being evaluated for how well they perform in real-world situations.
- Experience
- AI Customer Support Agent
Built a customer support agent for EliteCare Medical Center that handles patient inquiries, consultation payments, and cal.com appointment booking.
- Ad Metrics Automation
Built an automated Facebook ad metrics reporting workflow that sends performance metrics to Google Sheets, customer support agents for dental clinics on the Lukins Donkomi marketing team.
[03] | PROJECTS
Featured projects.
01
● LIVE
Lead Qualification AI Agent
An AI-powered WhatsApp agent that qualifies leads, captures them into a google sheet tool, and answers business-related questions.
- - Agent orchestration: Built the agent with LangChain to orchestrate LLM reasoning, retrieval, conversation state, and tool execution across the customer workflow.
- - Hybrid RAG retrieval: Combined semantic vector search (0.7) with lexical BM25 search (0.3) and used Reciprocal Rank Fusion (RRF) to improve retrieval across both meaning-based and exact keyword matches.
- - LLM evaluation: Built single-turn and multi-turn evaluation datasets to measure agent correctness and behavioral alignment across different conversation scenarios.
- - CI/CD pipeline: Automated agent evaluations on pull requests, enforced regression checks before merging, and deployed approved changes to production through Railway.
ChromaDB
Docker
Github Actions
FastAPI
LangChain
LangSmith
OpenRouter
Railway
02
● DEV
Legal Documents RAG
An AI-powered legal question-answering system built on the 1992 Constitution of Ghana, combining metadata and hybrid retrieval, reranking, citations, and automated evaluation to deliver accurate, grounded legal answers.
ChromaDB
Pinecone
FastAPI
Hugging Face
LangChain
OpenRouter
Ragas
[04] | STACK
My tech stack.
Technologies I use to build, evaluate, and deploy AI systems that work in the real world.
- LLM & Automation
- - Claude Agent SDK
- - Hugging Face
- - OpenRouter
- - Pydantic AI
- - LangChain
- - n8n
- Backend & Dev
- - TypeScript
- - FastAPI
- - Python
- - SQL
- Eval & Observability
- - LangSmith
- - Ragas
- Vector Store
- - ChromaDB
- - Pinecone
- DevOps & CI/CD
- - Github Actions
- - Docker
- - Git
[04] | STACK
My Tech Stack.
Technologies I use to build, evaluate, and deploy AI systems that work in the real world.
- LLM & Automation
- - Hugging Face
- - OpenRouter
- - LangChain
- - n8n
- Backend & Dev
- - Pydantic
- - FastAPI
- - Python
- - SQL
- Eval & Observability
- - LangSmith
- - Ragas
- Vector Store
- - ChromaDB
- - Pinecone
- DevOps & CI/CD
- - Github Actions
- - Docker
- - Git
OPEN TO WORK
[05] | CONTACT
Let's build something
{intelligent}
Currently open to staff-level roles and serious
freelance. The fastest way to reach me is email.