Mathematics/Algorithm Expert | AI/ML SaaS Engineer | Python Backend Developer | Automation & Workflow Specialist | GPT Copilot Builder

Hi,

I'm Arcade J Mamangun — a senior AI/ML Engineer (12+ years) who builds production LLM applications end-to-end: GPT-4/Claude integration, RAG systems, vector databases, model serving, and AI agent orchestration — with a strong track record shipping real AI products, not just prototypes.
Portfolio: https://arcadejmamangun.vercel.app
Resume: https://docs.google.com/presentation/d/13s1SQKps7bIhH3fIsOpW6CRu0PnUm_K0LrQkua7N8HA/edit?slide=id.p#slide=id.p
What you get with me isn't "plug-and-play ChatGPT wrappers." I design production-grade AI systems with proper architecture: RAG for accuracy, vector DBs for semantic search, prompt engineering for reliability, caching for performance, and monitoring for quality control.

What I'm strongest at (hands-on):
- LLM Integration: GPT-4o, Claude 3.5, custom prompt engineering, fine-tuning (LoRA), LangChain orchestration
- RAG Systems: Pinecone/Weaviate/Qdrant vector DBs, semantic chunking, hybrid search, 45% accuracy improvement
- Voice AI: Speech-to-text (Whisper), conversational AI, 1000+ daily call processing
- ML Deployment: FastAPI model serving, 500ms latency, Redis caching, load balancing
- AI Agents: Multi-agent orchestration, state management, tool use, conversation flows

Proven AI experience you can map to your project:
- Built conversational AI systems processing 1000+ daily interactions with custom fine-tuning
- Implemented RAG pipelines improving AI accuracy by 45% through semantic retrieval
- Deployed sentiment analysis models with 92% accuracy on production data
- Reduced inference latency from 2.5s to 400ms through optimization and caching

Real AI examples (live):
- SpecDoors AI: GPT-4 based specification generation with RAG over technical docs
- ReviewRevolution.ai: Multi-model sentiment analysis and review summarization
- Rentalizer.ai: ML-powered property valuation and market analysis
- Portfolio: https://arcadejmamangun.vercel.app

Tech stack:
- LLMs: OpenAI API, Anthropic API, HuggingFace Transformers
- Vector DBs: Pinecone, Weaviate, Qdrant, FAISS
- Frameworks: LangChain, LlamaIndex, PyTorch, TensorFlow
- Deployment: FastAPI, Docker, AWS, Ray Serve, MLflow

Here's what I've built:
- Voice AI automation systems processing 1000+ daily calls (Retell AI, VAPI, Twilio)
- n8n workflows reducing 85% of manual operations across sales/support teams
- Multi-platform integrations connecting 15+ CRM/API systems (GoHighLevel, HubSpot, Salesforce)
- Webhook architectures handling 10K+ daily events with 99% uptime

Examples of my automation work:
- Workflow Examples: https://drive.google.com/drive/u/0/folders/1hKEbAqZgzHMGBM

Availability: Full-time (or contract), remote, flexible across US/EU time zones with reliable real-time communication
Rate: $25/hour (negotiable based on project complexity)
If it helps, I'm happy to jump on a quick call to understand your AI requirements, constraints, and success criteria — then I can propose a clean architecture and delivery plan.

Best,
Arcade J Mamangun

Work Terms

Hourly Rate: $25
Availability: 30+ hours / week
Flexible for US/EU time zone