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IP FediuninFull-Stack & AI Engineer

For clients

Custom Python Backend, AI & Web Development

I design and build backends, AI features and automation for products and internal tools — and the web interfaces around them. Every service below links to case studies that show this kind of work.

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Services

  • Python Backend Development

    APIs, server-side logic and integrations in Python — with FastAPI, Django or Flask, chosen for the job.

    The problem
    Your product needs a backend that handles requests reliably, keeps data consistent and connects to the services around it.
    What you get
    An API and server-side logic: request validation, business rules, database access and integrations, with long-running work moved off the request path.

    Python · FastAPI · Django · Flask · PostgreSQL · Redis · REST API

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  • AI & RAG Solutions

    Question answering over your own documents, LLM features and vector search — built to cite sources and to say when the sources have no answer.

    The problem
    Knowledge sits in documents nobody can search well, or you want an LLM feature that answers from your data rather than from general knowledge.
    What you get
    A retrieval pipeline and an API around it: document processing and chunking, embeddings and vector search, and an LLM step that answers from the retrieved context with sources.

    Python · FastAPI · pgvector · Milvus · Redis · LLM APIs

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  • Full-Stack Web Applications

    Web applications with a React / Next.js interface and a Python backend, designed and built as one product.

    The problem
    You need a working product, not a standalone API and a set of pages that do not quite fit together.
    What you get
    An application where interface, server logic and data work as one: forms validated on both sides, server-side rules, transactions and responsive pages.

    React · Next.js · TypeScript · Python · Django · FastAPI · Flask · PostgreSQL

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  • Business Process Automation

    Python services and bots that take over repetitive work: collecting data, checking it, passing it on and reporting what happened.

    The problem
    People move data between systems by hand, mistakes surface late, and nobody can see the state of the process.
    What you get
    An automated flow: intake from APIs and webhooks, validation with clear rejection reasons, queued processing, stored results, notifications and a view of failures.

    Python · Django · RabbitMQ · REST API · Bots

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  • Data Processing & Analytics

    Numerical processing in Python: cleaning data, interpolation and numerical methods, and plots to check the results against.

    The problem
    Measurements arrive with gaps and outliers, at a step that does not match what you need, and the results have to be verifiable.
    What you get
    Processing tools that load, clean and transform data, restore the values in between with a suitable method, and produce plots and error metrics.

    Python · NumPy · SciPy · Pandas · Matplotlib

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  • Backend Integrations & Messaging

    Message brokers, API integrations and network communication between services and clients.

    The problem
    Work has to happen outside a single request, several services need the same events, or clients must exchange messages over the network.
    What you get
    Messaging between components: queues with competing workers, event logs with consumer groups, API integrations and socket-based client-server communication.

    RabbitMQ · Apache Kafka · Redis · Python sockets · TCP / UDP

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How I work

Every project goes through the same steps. Scope, timing and cost are agreed after discovery, when the problem is clear. The contract and invoices go through my individual entrepreneurship registered in Armenia.

  1. 01

    Discovery

    We clarify the problem, the people who will use the result, the data and the systems involved.

  2. 02

    Solution design

    I propose an architecture and the scope of a first working version, and we agree on it before building.

  3. 03

    Development

    The system is built in small steps you can see and comment on.

  4. 04

    Validation

    Each part is checked with tests, real runs and a look at the edge cases.

  5. 05

    Delivery

    The result goes into your repository and environment, with an explanation of how it works.

How to start

A first message does not need a specification. It helps to include:

  • What the system should do and who will use it.
  • The systems, data and services it has to work with.
  • Constraints you already know about: hosting, technologies, deadlines.

Have a system to build?

Describe the problem in a few sentences. I'll reply with questions and a proposed approach.

Discuss your project