Samuel P. looks like a good fit?
We can organize an interview with Aldin or any of our 25,000 available candidates within 48 hours. How would you like to proceed?
Schedule Interview NowSamuel P. AI, Cloud and Mobile Platforms
My name is Samuel P. and I have over 6 years of experience in the tech industry. I specialize in the following technologies: Machine Learning, TensorFlow, Python, PyTorch, Linux System Administration, etc.. I hold a degree in , Bachelor of Applied Science (BASc). Some of the notable projects I’ve worked on include: Cuelis – Prompt Management Platform for Automation & Agents, AI Customer Support Agent • RAG-Driven Ticket Resolution for Shopify, Vektaris – Decentralized AI-Ready Vector Database, ToolFlow Protocol JSON-First LLM Tool Discovery & Invocation Server, Hybrid Document Intelligence & Simulation Chatbot (MVP), etc.. I am based in Miami Beach, United States. I've successfully completed 27 projects while developing at Softaims.
I approach every technical challenge with a mindset geared toward engineering excellence and robust solution architecture. I thrive on translating complex business requirements into elegant, efficient, and maintainable outputs. My expertise lies in diagnosing and optimizing system performance, ensuring that the deliverables are fast, reliable, and future-proof.
The core of my work involves adopting best practices and a disciplined methodology, focusing on meticulous planning and thorough verification. I believe that sustainable solution development requires discipline and a deep commitment to quality from inception to deployment. At Softaims, I leverage these skills daily to build resilient systems that stand the test of time.
I am dedicated to making a tangible difference in client success. I prioritize clear communication and transparency throughout the development lifecycle to ensure every deliverable exceeds expectations.
Main technologies
AI, Cloud and Mobile Platforms
6 years
Machine Learning
4 Years
TensorFlow
3 Years
Python
1 Year
Additional skills
- Machine Learning
- TensorFlow
- Python
- PyTorch
- Linux System Administration
- Artificial Intelligence
- React Native
- React
- Next.js
- JavaScript
- Chatbot Development
- Node.js
- Firebase
- MongoDB
- Large Language Model
- Data Engineering
- Data Science
- Data Analysis
- Nextjs
- LLM
Direct hire
Potentially possible
Previous Company
Experience Highlights
Cuelis – Prompt Management Platform for Automation & Agents
I built Cuelis using TypeScript and React to simplify prompt management for clients running complex automation and agent workflows. Prompts often become difficult to track and update at scale, so I created a platform that allows me and my clients to easily organize, version, and update prompts on the fly—without rebuilding or redeploying the backend. This solution streamlines operations, reduces downtime, and enables fast iteration, making prompt-driven automation more reliable and efficient.
AI Customer Support Agent • RAG-Driven Ticket Resolution for Shopify
Developed an AI support agent that ingests open tickets from third-party sales platforms, normalizes them, and indexes key details into a vector store. Using a Retrieval-Augmented Generation pipeline atop GPT-4, it retrieves relevant FAQ content and historical responses to craft accurate, conversational replies. Integrated via Shopify webhooks and a lightweight React dashboard, the system logs all interactions in Firebase for auditability and continuously refines its knowledge base through periodic retraining.
Vektaris – Decentralized AI-Ready Vector Database
I built Vektaris as an in-house AI embedding storage system, which proved so powerful that I decided to release it publicly. Built on Internet Computer Protocol (ICP), Vektaris provides scalable, secure, and decentralized vector storage for AI-driven applications. 🔹 AI-Optimized Vector Databases – Store, manage, and query embeddings with high-performance retrieval. 🔹 Decentralized & Secure – Blockchain-based immutable data protection and end-to-end encryption. 🔹 Seamless Integration – Python, Node.js SDKs, and APIs for TensorFlow, PyTorch, OpenAI, and more.
ToolFlow Protocol JSON-First LLM Tool Discovery & Invocation Server
ToolFlow Protocol (TFP) is a lightweight, JSON-first spec and reference server—built in Node.js/TypeScript—for discovering and invoking “tools” (APIs) at runtime. • Endpoints: – GET /tfp/tools returns a manifest – POST /tfp/invoke calls a tool by ID • Type Safety: Zod runtime validation + TypeScript types • Extensible: Register new handlers with registerTool(descriptor, handler) • CLI-Friendly: ~150 lines—test instantly via curl Rapidly prototype context-aware toolchains for RAG workflows, custom LLM agents, or human integrations. See my GitHub for examples and setup instructions.
Hybrid Document Intelligence & Simulation Chatbot (MVP)
Built an end-to-end MVP using React & Firebase, OpenAI, and Pinecone to turn uploaded documents into live business simulations. Users chat or drag-and-drop PDFs, DOCX, or XLSX; Firebase handles state and storage. A Python preprocessing pipeline (OCR + format normalization) feeds Pinecone for vector search. OpenAI LLMs extract pricing rules, conditional logic, and parameters, then power a simulation API via FastAPI. Results and human-readable explanations return in a dynamic React interface, ready for rapid iteration and scaling.
Education
ITT Technical Institute
in Information Systems Cyber Security
2012-01-01-2015-01-01
University of London
Bachelor of Applied Science (BASc) in Artificial Intelligence
2021-01-01-2026-01-01
Languages
- English
Github