AI Developer & Full-Stack Engineering
Building robust, scalable AI-driven systems. I specialize in Python (FastAPI), React, Next.js, and LLM integration with Gemini and OpenAI. I build the core tech that powers modern agencies and B2B SaaS products.
Overview
I am not just a PM who codes; I am an AI Developer and Full-Stack Engineer. My technical sweet spot involves heavy lifting on the backend with Python (FastAPI) and crafting responsive frontends with React, integrated with cutting-edge AI models. I handle the entire lifecycle from database schema design to deployment on DigitalOcean or cloud VPS. I write clean, maintainable code that scales with your business.
Ideal For
- SaaS teams adding AI features for the first time
- Agencies needing senior backend support for client builds
- Startups replacing prototype code with production architecture
- Founders who need an AI integration partner, not just a coder
What's Included
- REST API Design & Implementation
- Database Architecture (PostgreSQL)
- Authentication & Authorization Systems
- LLM Integration (Gemini, OpenAI, Anthropic)
- Cloud Deployment (DigitalOcean / AWS / Vercel)
Benefits
- Scalable and secure architecture
- Full ownership of the tech stack
- Clean code that is easy to maintain
What You Get
- 1Production-grade FastAPI backend with documented OpenAPI specs
- 2AI features integrated end-to-end (LLM calls, streaming, function calling, RAG)
- 3Test coverage and CI/CD pipeline so deploys do not break things
- 4A handoff document so your team can own the codebase from day 90
Process
Architecture
Designing the system schema and API contracts.
Backend
Building robust APIs with FastAPI and SQLAlchemy.
Frontend
Connecting the UI to real data with React and Next.js.
DevOps
Setting up CI/CD and production environments.
Tooling & Stack
Common Questions
Which AI models do you integrate with?
Most engagements use Google Gemini, OpenAI GPT, or Anthropic Claude depending on the use case. I help you pick the right model for cost, latency, and quality — not the trendiest one. Vector search is typically Pinecone or pgvector.
Do you do RAG (retrieval-augmented generation)?
Yes. RAG pipelines with vector embeddings, semantic chunking, and hybrid search are a core part of most AI integration work. I have shipped RAG features for HealthTech, EdTech, and B2B knowledge tools.
Can you take over an existing codebase?
Yes. I take over messy or unmaintained codebases regularly — assessing tech debt, stabilising production, and incrementally refactoring without freezing feature delivery. Common stacks I have inherited: Django, Express, Flask, and various Python monoliths.
Do you handle infrastructure and deployment?
Yes — DigitalOcean droplets, Vercel, AWS (Lambda + ECS), Docker, and GitHub Actions are all in scope. I prefer simple, debuggable infrastructure over Kubernetes for small to mid-sized SaaS workloads.
