Ehtesham S.

"𝗬𝗼𝘂𝗿 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁 𝗶𝘀 𝗼𝗻𝗹𝘆 𝗮𝘀 𝘂𝘀𝗲𝗳𝘂𝗹 𝗮𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗳𝗲𝗲𝗱𝗶𝗻𝗴 𝗶𝘁" Did you know that AI-Ready data quality can prevent 38% of reported AI project failures? All you need is 1 engineer who understands both AI and the data behind it to build reliable AI systems with clean data pipelines, secure integrations, and failure handling built in... I build production AI/ML and agentic applications. From LLM-powered automation to real-time speech-to-speech AI platforms backed by a strong data engineering foundation that most AI developers lack I help SaaS, AI Fintech and enterprise teams design reliable systems across the full path: data ingestion to warehouse to orchestration to ML-ready datasets to LLM/RAG workflows to automation and decision systems 𝗛𝗘𝗥𝗘'𝗦 𝗛𝗢𝗪 𝗜 𝗖𝗔𝗡 𝗛𝗘𝗟𝗣 𝗬𝗢𝗨... - Agentic AI & LLM Systems: I build LLM-powered applications and agentic workflows that automate business processes, connect to internal tools and work with real operational data. This includes RAG/GraphRAG systems, embeddings, vector search, prompt workflows, retrieval evaluation, FastAPI-based AI services, and reliable orchestration around LLM pipelines - RAG, GraphRAG & AI Knowledge Systems: I can help you build retrieval systems that are grounded in your documents, databases and business rules. My data engineering background helps me design better ingestion, chunking, metadata, data quality and retrieval pipelines so the AI layer has clean and trusted information to work with - AI Ready Data Engineering: Your AI system is only as good as the data behind it. I design and optimize production pipelines using Python, SQL, Snowflake, dbt, Apache Airflow, BigQuery, AWS, and modern ELT patterns. I focus on reliability, monitoring, data quality, incremental processing, and clean data models that can support analytics, ML, and AI applications - ML Pipelines & Automated Decision Systems: I have experience building analytics and ML-enabled systems such as anomaly detection, behavioral scoring, feature pipelines, fraud/risk insights and operational monitoring. I focus on practical ML and automation that business teams can trust, understand, and use in production - Data Platform Modernization: If your dashboards are slow, pipelines fail silently, KPIs do not match, or your team spends too much time reconciling data manually, I can help modernize the architecture. I have worked on cloud data platforms, warehouse design, dbt modeling, Airflow orchestration, and KPI frameworks that make reporting and AI use cases more reliable 𝗥𝗘𝗦𝗨𝗟𝗧𝗦 𝗜 𝗛𝗔𝗩𝗘 𝗗𝗘𝗟𝗜𝗩𝗘𝗥𝗘𝗗 - Reduced ETL runtime from 35 minutes to 7 minutes through pipeline and query optimization - Improved reporting latency from 24 hours to under 15 minutes with modern warehouse and ELT design - Improved pipeline reliability to 99.8% with orchestration, monitoring, retries, and validation - Increased BI adoption from 20 to 80+ users by standardizing metrics and improving reporting access - Supported analytics and ML use cases across fintech, SaaS, procurement, media, and enterprise environments - Built data foundations for AI-ready analytics, anomaly detection, and LLM-powered retrieval workflows 𝗛𝗢𝗪 𝗜 𝗪𝗢𝗥𝗞? I build proper custom AI and automation systems the same way I build production data platforms: reliable, documented, testable and designed for real users. I care about architecture, maintainability, monitoring, data quality, and business outcomes 𝗧𝗘𝗖𝗛 𝗦𝗧𝗔𝗖𝗞: AI/LLM: RAG, GraphRAG, LLM Integration, Embeddings, Vector Search, Prompt Workflows, Retrieval Evaluation Backend/API: Python, FastAPI, REST APIs, Async Workflows, Automation Services Data Engineering: Snowflake, Dbt, Apache Airflow, BigQuery, SQL, Python, ETL/ELT, Data Modeling Cloud/Infra: AWS, Docker, CI/CD, Monitoring, Production Deployment Patterns Analytics/ML: Anomaly Detection, Feature Engineering, ML-ready Datasets, BI, KPI Frameworks P.S. If you need someone who understands both; data layer and the intelligent AI layer on top of it, I would be happy to discuss your workflow and recommend the most practical production-ready approach