I will build and optimize custom rag pipelines and llm workflows

V
vonplauen
V
vonplauen
Bohdan K
build and optimize custom rag pipelines and llm workflows
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build and optimize custom rag pipelines and llm workflows

About this gig

Is your AI pipeline too slow, expensive, or hallucinating when answering questions from your internal documents?


My proven track record includes optimizing a 7-stage OCR-to-verification pipeline, reducing document processing time by 6x (from 15 minutes to 2.5 minutes per document) and implementing intelligent caching/deduplication.


️ What I can do for you:

  • Custom RAG Architectures: Build local or cloud vector search systems using FAISS, ChromaDB, and custom chunking strategies.
  • Stop LLM Hallucinations: Tailor prompt engineering and domain-specific context retrieval to ensure 100% grounded, accurate answers.
  • Pipeline Acceleration: Implement smart caching and deduplication to skip reprocessing previously seen documents, saving you API costs.
  • Document Parsing & OCR: Extract clean, structured text from messy PDFs, DOCX, and scanned images.
  • Production-Ready Code: Deliver fully tested, containerized (Docker) Python pipelines ready for AWS (SageMaker/EC2) or local servers.

Tech Stack: Python, LangChain, FAISS, OpenAI / Claude / Local LLMs, Docker, Pandas.

  • AI engine

    • Claude
    • Gemini
    • DeepSeek
    • Hugging Face
    • Ollama
    • OpenRouter

  • Tools & frameworks

    • LangChain
    • LangGraph
    • LlamaIndex
    • OpenAI Agents SDK
    • n8n

  • AI solution type

    • AI agent
    • AI chatbot
    • Rag system
    • AI workflow
    • Internal AI tool

  • Purpose

    • Customer support
    • Marketing
    • Research
    • Workflow automation
    • Data analysis
    • Customer service
    • Ecommerce
    • Healthcare

Get to know Bohdan K

Bohdan K

AI and ML Engineer

4.9(14)
  • FromUkraine
  • Member sinceApr 2024
  • Last delivery11 months

  • Languages

    English, Ukrainian
I am an AI/ML Engineer with experience building intelligent systems through the implementation of automation and retrieval-based solutions. With a focus on turning unstructured data into reliable outputs, I work on designing LLM-powered pipelines and optimizing classical machine learning models for real-world accuracy. My expertise also extends to computer vision, where I build and tune models for real-time image classification tasks. I am committed to staying at the forefront of AI/ML developments, enabling teams to automate complex workflows and make faster, data-driven decisions.

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