DEV Community: Roman

AI/ML Engineer| Python, Node, FastAPI | LLM & API integration, n8n

Roman — Wed, 29 Oct 2025 15:01:57 +0000

I'm a Senior Software Engineer with 6+ years of experience building scalable backend systems, modern web applications, and AI-powered platforms that deliver real business impact.

🧠 AI/ML Expertise: I integrate advanced AI/ML solutions including LLMs (OpenAI, Anthropic), NLP, object detection, and predictive analytics — driving intelligent automation and user engagement.
🔧 Backend Engineering: My core stack includes Python (FastAPI, Django, Flask) and Node.js, building RESTful and GraphQL APIs, microservices, and secure, high-performance backends.
🧠 Automation & Integrations: I streamline workflows with Zapier, Make.com, and n8n, connecting AI systems, APIs, and databases to eliminate manual tasks and boost efficiency.
📈 SaaS & Data Platforms: From MVPs to enterprise systems, I deliver backend solutions with real-time processing, secure authentication, and scalable architecture — deployment-ready with Docker, AWS, and CI/CD pipelines.
🎨Frontend Designing: I also work with modern frontend frameworks like React, Vue.js, and Next.js when full-stack support is needed.

🔍 What I Deliver:

🔄Scalable backend APIs (REST, GraphQL) and microservices.
🤖AI integration with GPT-4, NLP, computer vision, and custom models.
⚙️Smart automation pipelines using Zapier, Make, and n8n.
⚡Real-time processing and async workflows.
☁️Cloud-native deployment Full DevOps cycle: Docker, AWS, GitHub Actions.
✍️Clean, maintainable, and well-documented code.

🛠️ Skills & Technologies

👉Programming Languages: Python, JavaScript, TypeScript, Go, PHP, SQL, C#
👉Backend Frameworks: FastAPI, Django, Flask, Node.js, Laravel, .NET Core, Express
👉Frontend frameworks: React, Vue.js, Next.js, and Angular.
👉AI/ML & NLP Tools: OpenAI API, Hugging Face Transformers, Scikit-learn, TensorFlow, PyTorch, spaCy, NLTK, Pandas, NumPy
👉Automation & Integration: Zapier, Make.com, n8n, REST API integration, Webhooks, Event Triggers
👉Database Systems: PostgreSQL, MySQL, MongoDB, Redis
👉DevOps & Cloud: Docker, AWS (EC2, Lambda, S3, DynamoDB), GitHub Actions, GitLab CI, Nginx
👉Other Tools: Git, Postman, JIRA, Figma, Trello, VS Code, Swagger

✅ Why Clients Work

With Me: 🎯Focused on backend excellence and ML-driven impact
🔁End-to-end project support — from idea to deployment
🔒 High attention to detail, security, and scalability
🗣️Clear communication and a long-term partnership mindset
🕒Strong track record of delivering production-ready
solutions on time
📩 Let’s build something powerful together — I usually reply within 5 hours.

Looking for a Patner

Roman — Wed, 29 Oct 2025 14:56:29 +0000

About me:
🌐 I am a senior Python software engineer with experience designing, developing, and deploying scalable backend systems, data pipelines, and AI-powered applications.

Role Overview:
🗣️ I need a native English speaker to represent me in client interviews and project meetings.
❌ No coding required, but excellent communication skills are a must!

What You’ll Do:
📅 Join client interviews & technical discussions
💼 Attend project meetings once jobs are secured
👤 Represent a team member’s profile professionally
🎥 Participate in live coding interviews with video on

You Should Have:
🗨️ Native-level English
💪 Confidence in client-facing conversations
👨‍💻 Comfortable in technical discussions and live coding sessions
🌎 Preferred location: North America, South America, or Europe

💰 Payment & Growth:
🤝 Once projects are secured, you’ll share the profit 30:70 for each successfully closed and completed project.
📈 As we grow together, there will be opportunities for steady pay and long-term collaboration.

Why This Is Awesome:
🏆 Gain client management & communication skills
💵 Long-term opportunity with stable pay later

✨ If this sounds like you, we’d love to hear from you!

🚀 How I Built a Multi-Agent AI Workflow System with n8n and Python

Roman — Wed, 29 Oct 2025 09:13:45 +0000

Introduction

Over the last few months, I’ve been working on an AI Agents Repository — a modular system where each agent handles a specific automation task using n8n, LangChain, and Python microservices.

The goal? To create a shared framework where developers can easily contribute new AI agents that solve real-world automation problems — from sales call prep to document RAG pipelines.

💡 Problem

Every automation project I’ve seen starts from scratch: new logic, new scripts, new integrations.
That’s fine for prototypes, but it doesn’t scale — especially when working with multiple AI workflows across clients.

I wanted a way to:
Reuse modular AI logic (LLMs, data pipelines, integrations)
Maintain consistency and quality across agents
Support fast iteration and collaboration across teams

🧠 Architecture Overview

+--------------------------+
| n8n Workflow |
| (Agent Orchestrator) |
+-----------+--------------+
|
v
+--------------------------+
| Python Microservice |
| (LangChain / FastAPI) |
+-----------+--------------+
|
v
+--------------------------+
| Shared Vector Database |
| (PGVector / Pinecone) |
+--------------------------+
Each agent runs inside n8n but delegates LLM logic to a Python microservice using FastAPI.
That microservice uses LangChain and a shared vector store for context retrieval and reasoning.

⚙️ Example Use-Case: Sales Call Prep Agent

The “Sales Calls Prep” AI Agent automatically gathers a prospect’s info, analyzes their company, identifies potential pain points, and produces a concise briefing for a sales call.

Inputs:
Prospect name, company domain, and LinkedIn profile

Outputs:
Short summary of the person and company
Pain points inferred from public data
Suggested call flow with value propositions
This agent runs daily for each active sales lead and outputs structured JSON + Markdown for human review.

🧩 Stack & Tools

n8n — visual workflow orchestrator
LangChain — LLM pipeline and tool calling
FastAPI — backend microservice
PostgreSQL + PGVector — semantic memory store
Docker Compose — for easy local development
OpenAI / Claude APIs — model layer

🔍 Lessons Learned

Workflow modularity is critical: separating orchestration (n8n) from logic (Python) simplifies debugging and versioning.
Schema validation saves time: strict JSON outputs make downstream automation reliable.
Vector databases matter: semantic search eliminates brittle keyword matching.

🌍 What’s Next

I’m currently extending this system to support:
Role-based access control for multi-tenant setups
Shared prompt libraries and reusable embeddings
Web dashboard for monitoring AI agent performance
If you’re working on anything similar — multi-agent systems, RAG pipelines, or workflow automation — I’d love to connect and exchange ideas.

🧑‍💻 About Me

I’m Roman Buhyna, a Senior AI Engineer & Full-Stack Developer (Python, FastAPI, React, LangChain, Docker, GCP).
Over 10+ years, I’ve helped teams build scalable backend systems and AI-powered products — from health-tech to workflow automation platforms.
Let’s connect on LinkedIn or GitHub — I’m always open to new collaborations and technical discussions.