I am a Data Engineer with four years of experience in building and maintaining ETL and ELT pipelines in AWS environments, leveraging tools such as S3, Glue, Athena, and Lambda. My expertise extends to data modeling and transformation using dbt, alongside orchestration with Apache Airflow. I possess strong skills in SQL Server and PostgreSQL, focusing on optimization and integration of operational sources to support analytics. My background includes governance, documentation, and ensuring data quality, as well as managing backup/restore routines and access controls. If you need a detail-oriented professional to streamline your data processes and enhance your analytics capabilities, let's discuss how I can contribute to your project's success.

✔️ 5+ years of experience in ML Engineering with deep specialization in AdTech, User Acquisition, and Mobile Gaming analytics ✔️ Expert in Python, PyTorch, TensorFlow, LightGBM, and GenAI (RAG, LangChain, Agentic AI) ✔️ Proven track record building LTV models, A/B testing frameworks, and attribution systems that optimize ad spend and maximize ROI 𝓐𝓫𝓸𝓾𝓽 𝓜𝓮 I am an ML Engineer based in Turkey, focused on data-driven user acquisition and AdTech optimization for mobile gaming. I don't just build models — I translate complex marketing data into actionable UA strategies. I have hands-on experience with MMPs (Appsflyer, Adjust), programmatic advertising platforms, and end-to-end AI/ML deployment. Whether you need LTV prediction, campaign performance optimization, or GenAI-powered marketing intelligence, I deliver solutions that directly impact ROI and user growth. 𝓗𝓲𝓰𝓱𝓵𝓲𝓰𝓱𝓽𝓮𝓭 𝓡𝓮𝓬𝓮𝓷𝓽 𝓟𝓻𝓸𝓳𝓮𝓬𝓽𝓼 1. Mobile Gaming UA Optimization Pipeline Built an end-to-end ML system for a mobile game publisher to optimize ad spend across 5 ad networks. Developed LTV prediction models using LightGBM and XGBoost on 18 months of user data. Implemented a multi-touch attribution model to allocate credit across channels. Achieved 27% reduction in CPI and 18% increase in ROAS within 2 months. Tech: Python, PyTorch, SQL, Apache Spark, Appsflyer API. 2. GenAI-Powered Ad Creative Performance Predictor Developed a RAG-based system using LangChain to predict ad creative performance before campaign launch. Extracted embedding features from ad images and copy using CLIP and BERT, then fed into a PyTorch regression model. Integrated with MMP data to validate predictions. Reduced creative testing cost by 40% and improved top-quartile CTR by 22%. 3. A/B Testing & Statistical Inference Framework Designed and implemented a robust A/B testing framework for UA campaigns at a gaming studio. Included power analysis, sequential testing, and causal inference using difference-in-differences. Built automated dashboards with SQL + Python to track significance in real-time. Empowered marketing team to make data-driven decisions faster, reducing test cycle time from 3 weeks to 5 days. 4. Real-Time Bidding Optimization with ML Created a real-time bidding (RTB) optimization engine for programmatic ad buying. Used PyTorch and TensorFlow to build a deep neural network that predicts click-through and conversion probabilities at millisecond latency. Deployed as a microservice with FastAPI. Achieved 31% improvement in win rate while maintaining target CPA. 𝓦𝓱𝓪𝓽 𝓘 𝓒𝓪𝓷 𝓓𝓸 𝓯𝓸𝓻 𝓨𝓸𝓾 ✅ Analyze large datasets from mobile games, ad networks, and MMPs to drive UA strategy and improve campaign effectiveness ✅ Develop predictive models (LTV, churn, conversion) and A/B testing frameworks to optimize ad spend and bidding ✅ Design, develop, and deploy end-to-end AI/ML solutions using PyTorch, TensorFlow, LightGBM, and Scikit-learn ✅ Implement GenAI applications using LangChain/LangGraph (RAG, Context Engineering, Agentic AI) ✅ Build SQL-based ETL pipelines and dashboards to track UA performance metrics ✅ Collaborate with marketing, UA, product, and engineering teams to translate business questions into analytical solutions ✅ Ensure accurate attribution tracking and measurement with MMPs (Appsflyer, Adjust) ✅ Stay current with AdTech, programmatic advertising, and gaming UA trends 𝓣𝓮𝓬𝓱𝓷𝓲𝓬𝓪𝓵 𝓢𝓽𝓪𝓬𝓴 - Machine Learning: PyTorch, TensorFlow, LightGBM, XGBoost, Scikit-learn, Scipy, Pandas, NumPy - GenAI & LLMs: LangChain, LangGraph, RAG, Vector Databases (Chroma, Pinecone), Context Engineering, Agentic AI - Data & Analytics: SQL (PostgreSQL, BigQuery, Snowflake), Apache Spark, PySpark, Hive - AdTech & UA: MMPs (Appsflyer, Adjust, Branch), Programmatic Platforms (DV360, The Trade Desk), Attribution Modeling - Deployment: Docker, Kubernetes, FastAPI, Flask, MLflow, AWS (SageMaker, S3, EC2) - Statistical Methods: A/B Testing, Causal Inference, Bayesian Methods, Power Analysis, Cohort Analysis - Visualization: Tableau, Power BI, Looker, Plotly, Streamlit - Other: Git, CI/CD, Airflow, DBT 𝓦𝓱𝔂 𝓦𝓸𝓻𝓴 𝓦𝓲𝓽𝓱 𝓜𝓮 ✅ 5+ years of hands-on ML engineering with specific focus on AdTech and mobile gaming UA ✅ Proven ability to deliver production-grade ML systems that directly impact ROI and user acquisition KPIs ✅ Deep understanding of both the technical ML side and the business/marketing context ✅ Strong communication skills — I translate complex data insights to non-technical stakeholders ✅ Reliable, available during IST overlap hours (9 PM IST to 12 AM IST) with flexible scheduling ✅ Based in Turkey, experienced working with global teams across time zones Let's optimize your UA campaigns with data-driven ML solutions. Send me an invite to discuss your project. Available for full-time, part-time, and project-based engagements.

I am a Data Engineer with over 2 years of experience specializing in the Snowflake ecosystem. I excel in building scalable ETL and ELT pipelines, data modeling, and crafting cloud data warehousing solutions across Azure and AWS. My expertise in technologies like PySpark, Snowpark, dbt, and SQL has empowered enterprise clients in healthcare and automotive to achieve seamless data migration and real-time analytics. I thrive on challenges and enjoy collaborating to deliver high-performance data solutions that drive actionable insights. If you need a proficient engineer who can effectively manage complex data environments and enhance your data infrastructure, I am ready to contribute to your success.

If you’re looking for an AI engineer who can design LLM and NLP systems that actually work in production, I build intelligent assistants, Chatbots, RAG pipelines and document answering tools that deliver accurate, reliable, enterprise grade results. Clients hire me to turn unstructured text, PDFs and domain knowledge into powerful AI systems that answer questions through grounded context. I’ve built LLM applications for EdTech, healthcare, HR and analytics startups across France, Netherlands and Germany. One client described me as “the best AI engineer I’ve worked with on Upwork” after I rebuilt their deep learning pipeline, fixed data leakage and pushed test accuracy beyond 90 percent. How I can help you: • Custom LLM assistants for your domain • Retrieval Augmented Generation (RAG) systems using your internal documents • Document Q&A pipelines for PDFs, reports, multilingual text and structured outputs • Chatbots for customer support, HR, education or internal workflows • Prompt engineering and evaluation frameworks for consistent results • FastAPI based backends, secure API deployment, and lightweight local LLM setups • Multi agent workflows, vector databases and orchestration with LangChain and LangGraph Tech I work with: Python, LangChain, LangGraph, HuggingFace, OpenAI API, FastAPI, FAISS, Chroma, PyTorch, TensorFlow, Google Cloud, Vertex AI, DeepL, SQLAlchemy Some results I’ve delivered: • Built a RAG system for medical text extraction that improved retrieval accuracy by 40 percent • Developed a multilingual document Q&A bot with memory and structured outputs adopted by analysts • Created an HR assistant with prompt versioning and evaluation tools that reduced response time 60 percent • Delivered AI assistants for educators that automate content review and student response analysis Why clients work with me: • Clean, documented, production ready engineering • Clear communication with both technical and non technical teams • Realistic timelines and solutions grounded in practical deployment • Experience in healthcare, EdTech, HR and enterprise document Let’s get started: Share your problem and a sample of your data. I’ll map out a simple action plan, suggested architecture and timeline so you know exactly how we’ll approach it. Keywords: LLM, NLP, RAG, Chatbot, Document AI, AI Automation, LangChain, LangGraph, Vector Database, Custom AI Assistant, Python, FastAPI, OpenAI.

TOP-RATED PLUS | 99% Job Success | 41+ Projects | 7+ Years Experience 🚀 Senior Software Engineer | Low-Code & No-Code Architect | Systems Integration Factotum | DevOps Python | OpenClaw | Odoo | n8n | Make | Zapier | Power Automate | Airtable | Low-Code | No-Code I am a versatile Software Engineer and Integration Factotum who specializes in connecting bespoke software ecosystems, engineering custom data pipelines, and bridging the gap between completely isolated business applications using a hybrid mix of custom code, low-code, and no-code platforms. I build production systems that actually work in real business environments—not just setups that look good on paper. My work sits at the intersection of backend engineering, DevOps cloud infrastructure, and low-code/no-code workflow automation. If your current software platforms do not communicate with each other, I build the custom middleware, API configurations, and automated data pipelines that bridge them seamlessly. I specialize in less-code and hybrid automation development—leveraging agile no-code visual builders for speed, while writing native Python and JavaScript code to bypass platform limitations. Whether you need to integrate a customized Odoo ERP environment, orchestrate autonomous OpenClaw multi-agent AI workflows, sync relational no-code hubs like Airtable, or connect disparate platforms via n8n, Make, Zapier, or Power Automate, I design the "glue" that makes your entire tech stack function as a unified system. 🔧 CORE AREAS OF EXPERTISE 1. Low-Code & No-Code Workflow Automation (n8n, Make, Zapier, Power Automate) Multi-Platform Orchestration: Designing and maintaining complex, fault-tolerant, low-code automation pipelines using n8n (self-hosted or cloud), Make (Integromat), and Zapier. Enterprise Windows Automation: Utilizing Microsoft Power Automate to connect legacy desktop applications, SharePoint, and Microsoft 365 environments with modern cloud platforms via low-code workflows. Hybrid "Less-Code" Systems: Writing custom Python and JavaScript scripts directly within n8n, Make, and Zapier workflows to bypass native no-code limitations, handle complex data loops, and bypass API rate limits. Rapid Prototyping: Deploying agile, maintenance-friendly no-code triggers and actions to automate manual business operations with minimal friction. 2. Relational No-Code Databases & E-Commerce Infrastructure (Airtable, Shopify, Ecwid) Airtable Database Architecture: Designing highly optimized relational schemas, advanced formulas, low-code scripting blocks, and native no-code automations inside Airtable to act as an operational backend or single source of truth. E-Commerce Pipeline Engineering: Syncing digital storefronts like Shopify and Ecwid with central inventory systems, tracking orders, managing customers, and automating downstream fulfillment. Unified Commerce Data Flows: Linking Shopify and Ecwid APIs to low-code tools like Airtable, HubSpot, Zoho, and Odoo via n8n and Make to eliminate manual data entry. 3. Bespoke Software, API Architecture & CRM Integration Custom Integration Frameworks: Connecting proprietary, legacy, or niche business applications that completely lack native, out-of-the-box low-code or no-code integrations. CRM Architecture & Syncing: Advanced API configuration, data mapping, and automated workflow development inside enterprise CRMs like HubSpot and Zoho. Cross-Platform Synchronization: Building bi-directional, real-time data syncs between HubSpot, Zoho, Odoo ERP, and external custom backends using webhooks, APIs, and low-code web controllers. 4. Enterprise Odoo ERP Customization & Core Development Full Odoo Deployments: Comprehensive Odoo ERP setups across Sales, Inventory, Purchase, CRM, Accounting, and Manufacturing. Custom Odoo Modules: Advanced Odoo development creating tailored Python models, XML views, and proprietary business logic when no-code features aren't enough. Odoo Integration Pipelines: Direct API connection linking Odoo to Airtable, Shopify, Ecwid, HubSpot, and Zoho using native endpoints, custom middleware, low-code n8n logic, or Zapier. 5. AI, LLM Integration & OpenClaw Agentic Frameworks Agentic AI Ecosystems: Building production-grade multi-agent AI networks and autonomous decision-making loops using OpenClaw. OpenClaw & Low-Code Hybridization: Integrating OpenClaw AI agents directly into low-code/no-code automation platforms like n8n, Make, and Zapier for intelligent document classification, data extraction, and routing. Contextual AI Pipelines: Feeding data from Airtable, HubSpot, Zoho, and Odoo directly into OpenClaw-driven RAG systems to build highly customized, domain-specific AI assistants. 6. Transactional & Marketing Email Automation (SendGrid, Mailchimp) Email Infrastructure Management: Setting up, configuring, and scaling SendGrid and Mailchimp APIs for reliable transactional and marketing delivery. Event-Driven Journeys: Building triggers (e.g., Shopify abandoned cart, Hu

I build robust, production-grade data infrastructure that turns complex, high-volume data into systems teams can actually trust and use. With 15+ years of experience as a Senior/Lead Data Engineer, I've delivered end-to-end data engineering initiatives across fintech, banking, energy, e-commerce, and pharma for organizations including major retail and online banking institutions, ExxonMobil, Schneider Electric, Philip Morris International, Mastercard/Visa card-processing systems, and global pharmaceutical clients. My core expertise is building scalable batch and streaming pipelines on Databricks using PySpark and Scala, and designing lakehouse architectures with Delta Lake and Unity Catalog across the full Medallion (Bronze/Silver/Gold) stack. I work across both AWS and Azure, with deep hands-on experience in Azure Data Factory, Azure Synapse Analytics, and cloud migrations to platforms like Google Cloud. I'm equally comfortable owning platform architecture — cluster configuration, governance, environment management across dev/staging/production — as I am writing the pipeline code itself. A sample of measurable outcomes from recent engagements: Architected Databricks pipelines processing high-volume transactional data for a major online banking platform, supporting core banking processes including payments, reconciliation, and regulatory reporting, while maintaining full data lineage and auditability under financial services compliance standards (FSA/FISC). Improved Spark pipeline performance by 30% through targeted tuning in a Databricks + Azure environment, and refactored a monolithic codebase into a domain-driven structure to boost long-term maintainability. Reduced report processing time from 24 hours to 6 hours by redesigning a cross-region S3 data migration pipeline. Cut a critical processing job from 6 hours down to minutes through a targeted Scala-based optimization on a Spark/Kafka pipeline. Built a scalable Scala-based pipeline to process card transactions from legacy systems used by major providers (Mastercard, Visa), integrating results into Power BI dashboards that significantly reduced fraud detection time. Led the formation and upskilling of three cross-functional teams (3–5 engineers each) within 3 months, transforming SQL-focused analysts into capable Scala/Python/Big Data engineers, then mentoring them toward full autonomy. Designed and implemented ML pipelines and migrated Python ML scripts to Scala for improved speed and integration, working across the Cloudera platform. Delivered GDPR-compliant ETL migrations to Google Cloud, and built reporting layers using Zeppelin, HUE, and Power BI for business stakeholders across finance, operations, and compliance. What I bring to a project: Scalable batch & streaming pipeline design (Databricks, PySpark, Scala, Spark Structured Streaming) Lakehouse architecture: Delta Lake, Unity Catalog, Medallion Architecture CDC, data quality, and governance in regulated environments Cloud platform expertise across AWS and Azure (ADF, Synapse, Databricks) dbt for transformation and data modeling Cross-functional collaboration — translating ambiguous business requirements into solid, maintainable architecture Technical leadership and mentoring, having built and upskilled engineering teams from the ground up I hold a Master's degree in Information Systems from Northeastern University, an Azure Data Engineer certification, and additional certifications in Scala functional programming and secure coding. I'm currently based in Tokyo and have also worked across Spain, Portugal, Norway, and Denmark, giving me experience adapting to different regulatory and team environments. If you need a dependable partner to design, build, or rescue a data pipeline — someone who can own the architecture end-to-end and communicate clearly with both engineers and business stakeholders — let's talk about your project and how I can help.

Senior Cloud Data Engineer with 5+ years of hands-on experience building production-grade ETL/ELT pipelines, data warehouses, and BI solutions. I specialize in Apache Airflow, dbt, Spark/PySpark, Snowflake, BigQuery, and AWS/GCP/Azure. I've delivered end-to-end data solutions from raw ingestion to executive dashboards for businesses that rely on accurate, scalable data. Whether you need a robust data pipeline, a Snowflake warehouse, or Power BI dashboards, I deliver clean, maintainable, and well-documented work.

Trust AI is a Lima-based software studio. Senior team. 5+ years building production software, AI systems, data platforms, and full-stack products. We design and ship custom software platforms, AI automation systems, and LLM-powered tools that cut operational costs by 40%+ production-deployed in weeks, not months. If you need software or AI that actually works inside your business, not a proof of concept that sits on a shelf, you are in the right place. ✅ WHO WE WORK WITH BEST Companies with real operational bottlenecks who want technology to solve them: manufacturers, fintechs, tourism operators, and e-commerce businesses that process data, documents, or workflows manually and know there is a better way. ⚠️ THE PROBLEMS WE HEAR MOST "Our last dev disappeared mid-project." "We don't know if AI will actually work for our use case." "We've wasted months building the wrong thing." "We need someone who can ship, not just prototype." We fix all of that. The same team that scopes your project ships it, supports it, and stays accountable after launch. 🚀 WHAT WE DELIVER - Custom platforms and internal tools: B2B products and internal systems built end-to-end with full accountability - Agentic AI workflows: autonomous agents that execute complex multi-step processes reliably in production, not in demos - Conversational AI: production-grade assistants wired to your actual systems and business logic - AI automation pipelines: connect your business data to intelligent workflows that run themselves - RAG systems: let your team ask questions and get answers grounded in your own documents - Data platforms and dashboards: pipelines and models that turn raw data into decisions 📊 REAL RESULTS FROM REAL CLIENTS Reduced document review costs by 67% for a manufacturing client (Owens-Illinois) Increased customer retention by 25% with AI-powered recommendations (uDocz) Cut operating costs by 15% via automated data migration (Indeed / BCP) Production systems shipped for: CPG (Ajinomoto), Tourism (Inca Rail), Fintech (Finniu), Ecommerce (Cohynsa, Zafiro) ❌ NOT A GOOD FIT IF You need the cheapest option, want a solo contractor with no accountability, or need someone to start coding before the scope is clear. Let's talk about what you're building.

I am a data engineer and software engineer with a strong focus on building reliable data systems in distributed and production environments. I work across the full data lifecycle, from data ingestion and transformation to modeling, deployment, and monitoring. My experience includes designing scalable data pipelines, working with cloud and containerized infrastructures, and collaborating closely with data scientists and product teams to turn complex data into actionable systems. I am comfortable operating in high volume, low latency environments and enjoy solving problems related to data quality, performance, and system reliability. I have a solid background in Python and experience with distributed technologies such as Kafka, Spark, Kubernetes, and cloud platforms. On the data science side, I work with statistical analysis and machine learning when it brings real value, always with a focus on production readiness rather than experimentation only. I value clean architecture, well tested code, and clear communication. I am used to working autonomously while staying aligned with stakeholders, and I enjoy taking ownership of systems that need to work reliably at scale.

I build production AI, Machine Learning, and Data Analytics systems that cut operational costs by 40%, prevent ~$2M in annual fraud losses, and turn messy enterprise data into revenue-driving Business Intelligence. Recent work includes a Machine Learning microservices platform serving 2M+ users and 100M+ daily API requests, a multilingual AI Agent handling 15,000+ daily messages with a 20-point NPS lift, and a RAG-powered AI Chatbot embedded in production web apps that drove a 42% conversion rate increase. I work end-to-end across Generative AI, LLM Engineering, AI Agent Development, AI Automation, Data Science, Data Engineering, ETL Pipelines, MLOps, and executive Dashboards in Power BI, Tableau, Looker Studio, and Google Analytics 4. The teams I work best with have data and ambition but no roadmap to ship AI in production without breaking compliance, scale, or budget. What I build: • GPT and Claude-powered AI Chatbot Development, AI Agent Development, and LLM Integration using OpenAI API, Anthropic Claude API, Google Gemini, LangChain, LlamaIndex, and RAG with Pinecone, Weaviate, and Chroma vector databases • Machine Learning and Deep Learning models for fraud detection, demand forecasting, customer segmentation, recommendation engines, and NLP • ETL Pipelines and Data Engineering on Google BigQuery, Snowflake, Databricks, AWS, Azure, and SQL • Power BI Dashboards, Looker Studio Reports, Tableau Visualizations, and full Google Analytics 4 implementation • Python Automation, Web Scraping, API Integration, and AI Workflow Automation for scalable business processes Selected results in AI, Generative AI, and Machine Learning: • GPT-powered AI Chatbot reduced support tickets 50% in one quarter • Machine Learning microservices platform serving 2M+ users and 100M+ daily API requests • Multilingual AI Agent handling 15,000+ daily messages, lifting NPS 20 points • Deep Learning fraud detection model cut false positives 40%, preventing ~$2M in annual losses • Predictive inventory AI decreased stockouts 30% and real-time customer segmentation lifted conversion 28% • NLP-based ticket triage automation improved first-response efficiency 65% • RAG-powered AI Chatbot embedded in web apps improved conversion 42% • Real-time AI Call Center Assistant cut average handle time 35% and lifted first-call resolution 22% • Predictive AI for cell tower failures reduced emergency dispatches and improved network uptime Selected results in Data Engineering and ETL: • Migrated Data Engineering stack to Google BigQuery: 60% faster queries, 25% lower storage costs • Standardized ETL Pipelines for Data Mining and Data Annotation, increasing ML training throughput • Built feature stores and reproducible MLOps training pipelines that accelerated ML iteration • Self-correcting Data Analysis pipeline reduced reporting errors 95% • Python competitive monitoring system mining 150+ sources daily into Google Sheets and BigQuery • SQL clustering and replication deployed for 99.99% uptime on mission-critical apps Selected results in Dashboards and Business Intelligence: • Looker Studio Dashboard suite reduced delivery delays 15% • Marketing Analytics Dashboards unifying paid media, web, and CRM data in Power BI and Looker Studio • C-level KPI Dashboard pack (SQL + Google Analytics 4 + Excel) accelerating weekly decisions • Server-side Google Tag Manager implementation significantly improved GA4 accuracy • Pricing optimization engines lifted profit margins 17% • Self-serve Analytics portal with NLQ-powered Power BI reduced ad-hoc requests 55% • JavaScript bridge syncing legacy Excel models with cloud databases, saving 20 hours per week Beyond implementation, I run AI Readiness Audits for executive teams that have invested in data infrastructure but not yet seen ROI, identifying high-impact AI Agent Development and Machine Learning opportunities and translating them into a 90-day rollout plan with clear success metrics and risk gates. Tech stack: Python, SQL, JavaScript, TypeScript, AWS, Google Cloud, Azure, BigQuery, Snowflake, Databricks, dbt, Airflow, Power BI, Tableau, Looker Studio, GA4, Google Tag Manager, Excel, OpenAI API, Anthropic Claude API, Google Gemini, LangChain, LlamaIndex, Hugging Face, TensorFlow, PyTorch, scikit-learn, Machine Learning, Deep Learning, Generative AI, LLM Engineering, MLOps, NLP, ETL Pipelines, Data Mining, Data Annotation, Data Visualization, AI Integration, AI Automation, AI Agent Development, AI Chatbot Development, RAG, and Vector Databases (Pinecone, Weaviate, Chroma). Share your data sources, target metrics, and timeline. I will reply within 24 hours with a practical rollout plan covering ETL Pipeline through to production AI Integration, plus a short list of risks I see before we start. I work as a strategic partner on production AI and Data systems, not a ticket-taker.