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Need to build an AI system over your data (RAG, LLM workflows, automation) — not just a demo, but something that works reliably in production? I design and deliver AI-powered backend systems: RAG pipelines, LLM apps, semantic search, and automation integrated with real business data (APIs, databases, documents, search indexes). Recently, I’ve built systems combining OpenAI / Anthropic / DeepSeek with Pinecone, OpenSearch, Snowflake, PostgreSQL, LangChain, and async pipelines (Celery) for knowledge search, lead processing, messaging, analytics, and automation workflows. 16+ years building production SaaS, high-load APIs, and data-heavy systems. --- 🤖 AI Systems & LLM Work - RAG pipelines over structured + unstructured data (embeddings, vector search, semantic search, contextual answers) - OpenAI-powered knowledge assistant using embeddings + Pinecone vector retrieval + context-aware prompt construction - LangChain SQLDatabaseChain integration for PostgreSQL-backed fallback retrieval - LLM-powered workflows (prompt pipelines, structured outputs, streaming responses) - Real-time streaming AI chat using Django StreamingHttpResponse / SSE-style responses - AI integrated into production systems (Celery, async jobs, retries, batching, monitoring) - Model routing with company/prompt-level fine-tuned model support and fallback handling - Real-world use cases: enrichment, classification, messaging automation, Q&A, data processing --- 🧠 What I can build for you - RAG systems over your internal data (documents, databases, search, QA, assistants) - AI-powered knowledge assistants and semantic search tools - LLM-powered automation (lead processing, emails, enrichment, classification) - Internal AI tools for operations, support, analytics, and data workflows - Backend systems integrating LLMs into real production workflows (not isolated scripts) --- ⚙️ Backend, APIs & System Design - Python, Django, Django REST Framework, FastAPI - API design, auth, permissions, business logic orchestration - Webhooks & integrations (Stripe, SendGrid, Nylas, Zapier, OpenAI, etc.) - High-load async systems (Celery + Redis, queues, retries, scheduling) - Production-safe error handling, fallback logic, logging, and debugging --- 📊 Data, Search & RAG - PostgreSQL/Aurora, Snowflake - OpenSearch / Elasticsearch, Pinecone - Embeddings, vector search, semantic search, context retrieval - Data modeling, ETL pipelines, complex SQL, performance tuning - Enrichment, deduplication, segmentation, scoring systems --- ☁️ Infrastructure & Delivery - AWS (S3, Lambda), Docker, Nginx - CI/CD, production deployments, monitoring - Reliable systems for high-throughput and data-heavy workloads --- 🖥️ Frontend (when needed) - React, TypeScript - Dashboards, admin tools, API-driven interfaces - Frontend integration with AI-powered backends and streaming responses --- 🎓 Applied Mathematics & Computer Science Background - Mathematical modeling of real-world systems - Probability, statistics, numerical methods - Optimization, algorithms, system-level thinking This foundation helps design efficient, scalable AI systems (RAG, pipelines, search, data workflows). --- 📜 Certifications & Learning - Generative AI for Software Developers Specialization - LangChain for LLM Application Development - Claude Code in Action (Anthropic) - RAG / AI Systems / AWS certifications in progress --- I focus on building systems that actually work in production — reliable, scalable, and aligned with real business workflows. If you need AI integrated into your backend, data, search, or automation systems, I can help.

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.

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.

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.

Are you seeking a Jira expert who not only understands the technical intricacies but also aligns configurations with your business objectives? I'm a Jira Administrator with over 5 years of experience in configuring and optimizing Jira environments to enhance team productivity and project transparency. My expertise lies in tailoring Jira setups that streamline workflows and facilitate effective collaboration. Services Offered: - Custom Jira project setups (Cloud & Server) - Designing and implementing complex workflows, screens, and permission schemes - Developing advanced automation rules to reduce manual tasks - Integrating Jira with Confluence, Bitbucket, GitHub, and CI/CD tools - Creating insightful dashboards and reports for stakeholders - Managing user access and security protocols - Providing user training and support to ensure effective Jira utilization I take pride in delivering solutions that not only meet technical requirements but also drive business value. Let's collaborate to make Jira a powerful asset for your team.