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AI Engineer Who Ships Production Systems | ex-clients Netflix, SAP, Adidas Your AI project is stuck between "impressive demo" and "actually works in production." The integration is fragile. The RAG pipeline hallucinates on real data. Costs are climbing faster than value. And every week it stays in this state, the business case gets harder to defend. You don't need another AI enthusiast who learned LangChain last year. You need an engineer who's been building scalable systems for over a decade — and who's shipped 10+ AI systems that are running in production right now, not sitting in a graveyard of abandoned pilots. I'm Vahe. I build production-grade AI systems as a lead AI engineer with 10+ years of engineering experience for companies like Netflix, SAP, Adidas, Liberty Mutual, EPAM, and EBSCO. Here's what I've built and shipped: 👉 Multi-agent orchestration systems — production workflows where multiple AI agents coordinate end-to-end: triaging incoming requests, routing to specialized agents, executing actions, and self-correcting when something breaks. Not a demo chain of prompts — real systems with fallback logic, cost controls, and observability built in. 👉 RAG pipelines that survive real data — retrieval-augmented generation systems that handle messy, contradictory, and incomplete enterprise documents without hallucinating. Semantic search with hybrid retrieval, re-ranking, and grounded citation — built to scale, not to impress in a controlled test. 👉 Intelligent document processing — AI-powered extraction, classification, and routing of complex documents at scale. Invoices, contracts, medical records, regulatory filings — structured output from unstructured chaos, with confidence scoring and human-in-the-loop where it matters. 👉 AI-powered chatbots and assistants — customer-facing and internal conversational systems that handle real conversations, not scripted happy paths. Context management across long sessions, graceful escalation to humans, and integration with existing business systems. Why most AI projects fail — and why mine don't: The dirty secret of AI engineering is that the AI is the easy part. 90% of making these systems work in production is everything underneath: architecture that handles failure gracefully, error recovery that doesn't lose data, token optimization that keeps costs from exploding, security that protects sensitive data flowing through LLM APIs, and observability that tells you exactly what went wrong at 3 AM on a Saturday. That's not something I learned to do for AI. That's what I've been doing my entire career as a senior engineer. AI just gave me a new class of problems to solve with the same engineering discipline. My stack: AI & LLM: AI agent development, RAG systems, LangChain, LangGraph, context engineering, fine-tuning, OpenAI API, Claude API, OpenClaw, vector databases (Pinecone, ChromaDB), MCP integration Infrastructure: AWS (Certified Solutions Architect), Docker, CI/CD, PostgreSQL, Python, Java, Go What happens when you message me: I'll set up a free 30-minute architecture review call. You tell me what you're building — or what's broken. I'll tell you exactly where the risks are, what's going to cause problems at scale, and where AI-driven workflows could save you the most time and money. No pitch, no slides. Just a technical conversation between engineers about how to make your system work. If it makes sense to work together, I'll scope the engagement and we start building. If it doesn't, you still walk away with a clearer picture of your AI architecture — on me.

I build production-ready AI agents and retrieval-augmented generation (RAG) systems backed by robust backend architecture. With strong expertise in Python, FastAPI/Django, vector databases (Pinecone, Weaviate, Qdrant, Chroma), and LLM frameworks (LangChain, LlamaIndex), I turn raw language models into reliable, scalable automations. What I deliver: - Custom RAG pipelines for private documents (PDFs, websites, databases) - LLM agents with tools, memory, and structured outputs - API integrations (OpenAI, Anthropic, Groq) plus open-source models (Llama, Mistral, Gemma) via Ollama, HuggingFace, or Replicate - Production deployment (Docker, cloud, async processing) - Evaluation & monitoring (Ragas, LangSmith, custom metrics) Why me: I don't just prototype notebooks. I build testable, maintainable, and secure systems—backend best practices applied to LLMs. You get low hallucination, high accuracy, and a clear path to production. Need to run Llama, Mistral, or Gemma locally for data privacy or cost control? I handle that too.

Senior engineer with 15+ years building scalable platforms and, recently, production-grade LLM and Retrieval-Augmented Generation (RAG) systems. I architect data ingestion pipelines, embedding and retrieval layers, and structured inference workflows on top of reliable backend infrastructure. Strong focus on efficient retrieval design, secure multi-tenant data access, prompt engineering for structured outputs, and cloud-native deployment of AI services. Core Skills - LLM/RAG: LangChain, LlamaIndex, custom orchestration flows Vector Databases: Pinecone, pgvector, Weaviate - Backend: Python (FastAPI, Django), Node.js - Cloud: AWS, GCP, Docker, Kubernetes Strengths: scalable architecture, clean documentation, production reliability GitHub: github.com/brnikita

I help companies build AI systems that extract, validate, and route data from PDFs, scans, emails, spreadsheets, and internal knowledge bases into the tools their teams already use. My main focus is AI document processing, OCR, data extraction, and RAG-based automation for real business workflows. This usually means combining OCR, LLMs, retrieval pipelines, validation rules, human review, APIs, and CRM/ERP/database integrations into one reliable process — not just building a standalone AI demo. Together with the Businessware Technologies team, I have worked on medical form OCR, scanned receipt extraction, bank statement parsing, technical document layout extraction, newspaper OCR and segmentation, Azure Document Intelligence customization, RAG agents, AI assistants, and document processing pipelines for legal, healthcare, finance, logistics, and operations teams. A typical project starts with understanding the business process: what documents come in, what data needs to be extracted, where errors happen, who reviews the result, and where the final output should go. From there, we design and build a practical system that fits your data quality, budget, timeline, and risk level. My skills: Data Extraction, OCR Software, Document Analysis, AI Development, Intelligent Document Processing, Document Automation, Retrieval-Augmented Generation, OpenAI API, Python, FastAPI, LangChain, Vector Database, Azure AI Document Intelligence, AWS Textract, API Integration, Workflow Automation, AI Agent Development, Computer Vision, Natural Language Processing, n8n.