Enes B.Private profile: Upwork shows its earnings only to signed-in members, so we check it every few weeks, not daily. MRR here is a rough estimate (Upwork's six-month earnings ÷ months active). Last read Nov 5, 2026.

Top 3% Freelancer who built enterprise multi-tenant RAG systems with custom LLM deployments, reduced LLM workflow costs by 70%, and delivered a 2M product data quality pipeline for a Fortune 500 biotech company, reducing manual efforts by 85%. Architected and deployed production systems across finance, biotech, energy, telecommunications, and healthcare. 5+ years taking end-to-end ownership on all projects, from greenfield architecture to secure & scalable production deployment, leading engineers and maintaining systems long-term. I specialize in production-ready AI solutions with measurable ROI. From 90% accuracy voice classification to billion-scale data engineering. Core Expertise: • AI Agents / Agentic Workflows (LangGraph, Tool Calling, Function Calling, Agent Memory Patterns, Agent Patterns, Multi-Agent Orchestration, Building MCP, Plugins, Skills, Hooks, Deterministic Enforcement, ReAct Pattern, AI Assistants, Research, Planning, Writing) • LLM Systems (Claude API, OpenAI API Integration, vLLM, LoRA/QLoRA Fine-Tuning, PEFT, RLHF, DPO, LLaMA, Mistral, Prompt Engineering, Deterministic Workflows, LLM Evaluation, Cost Optimization, Deploying Chatbots, Deploying LLMs, Claude Code, Codex) • RAG (Hybrid Retrieval, Vector Databases, Evaluation Harness, Tiered Access, Custom Document Parsing, PDF/Clinical Data Extraction, Ragas, Multi-Tenancy) • NLP (Transformers, spaCy, Named Entity Recognition, Text Classification, Taxonomy Design, Rule-Based Extraction, Sentiment Analysis, Topic Modeling, Summarization, Information Extraction) • Generative AI (Structured Outputs, Pydantic Validation, Guardrails, Hallucination Prevention, AI Chatbot Development, AI Integration) • ETL & ML Pipelines • Time Series Applications at scale. - I personally prefer native APIs for building Agentic / LLM workflows. Domain Expertise: • Energy & Utilities (demand forecasting, carbon emissions, time series, pricing optimization) • Financial Services (compliance-ready RAG, churn prediction) • Life Sciences (biotech product classification, scientific NER, clinical data extraction, safety) • Telecommunications (voice classification, Whisper, ASR, diarization) Projects - Open source contributor (Langchain vLLM integration fix) - Enterprise RAG Engine with multi-tenancy, project-level isolation, custom vLLM deployments, agent live on the WhatsApp channel - customer support. - Finance-Specific RAG System with compliance features and RAG benchmarking - Enterprise Knowledge Base Chatbots Integrating (RAG daily sync on Notion, Slack, Website, Documents, evals, Live on Zendesk Customer Support Agent, Internal AI Agents on Slack(Deep Research, Q-A, Reporting), Blogpost creation with tone and fact checks.) - Production agentic workflows with state management, semantic processing, and monorepo CI/CD - SEO keyword clustering system processing 10k+ keywords under 10s using OpenAI embeddings + HDBSCAN - Enterprise Misspelling Detection Pipeline for 2M biotech products using FAISS similarity matching and 740k scientific corpus validation (Thermo Fisher) - Real-time Voice Classification System (90% accuracy, active learning pipeline) - Call Recommendation System with statistical engine for optimal calling times - Energy Market Forecast System for Budapest electricity prices - 500+ Class Text Classification with Fine-Tuned BERT - Extending a Lexicon using GloVe Word Embeddings - Databricks PySpark Data Pipeline for Aggregating Billion Scale Data - Time Series Analysis and Models on Energy Consumption Data from different locations in Belgium and Germany - API Integrations & Calculations on Electricity Business Objectives - Imbalanced Binary Classification on Anonymized Data (Oversampling + CatBoost & hGBM) - Data Processing Pipeline for 70m of Electricity Sensor Data for a Research Project Tech Stack: • Programming Languages: Python, SQL • Machine Learning & Deep Learning: TensorFlow, Keras, PyTorch, Scikit-Learn • NLP: LangChain, NLTK, Gensim, FAISS, Pinecone, pgvector • Time-Series: Prophet, TSMixer, ARIMA, SARIMAX, LSTM • MLOps & Data Engineering: Terraform, Linux, Apache Spark, Airflow, Docker, Flask, Streamlit, Gradio, MLflow, W&B, Optuna, CI/CD Pipelines, n8n workflows, Ragas • Cloud: Google (BigQuery, Cloud Functions, Cloud Run, Dataflow, Vertex AI, Firestore, Cloud Storage, Composer) • AWS (Lambda, SageMaker, S3, EC2, ECR, DynamoDB, Glue, CloudWatch) • Azure (Databricks-PySpark) • Database: PostgreSQL, Pinecone, Chroma • Data Manipulation & Statistics: Pandas, NumPy, OpenCV, Regex, SciPy, Statsmodels • Data Visualization: Matplotlib, Plotly • Web Scraping: BeautifulSoup, Requests • Building Custom NER Models & Linking, Question Answering, Text Similarity & Word Embeddings, Part-of-Speech Tagging & Dependency Parsing, Text Generation, Lexical Semantics & N-grams, Clustering • Logistic Regression, SVM, Decision Trees, Random Forest, Gradient Boosting Machines (XGBoost, LightGBM, hGBM), k-NN, CNN, RNN, K-Means, DBSCAN, HDBSCAN, OPTICS