Danish M.

AI & Data Engineering Expert | Workflow Automation & Cloud Solutions I help businesses scale faster with AI-driven workflows and robust data engineering solutions. With over 10 years of experience, I design agentic AI systems that think, decide, and act across your tools, automating customer support, payment flows, reporting, and decision-making processes. As a data engineering specialist, I build scalable pipelines, integrate enterprise systems, and deliver analytics solutions using AWS, Azure, GCP, Databricks, Airflow, Airbyte, dbt, and advanced data modeling. I bridge the gap between technical complexity and business impact to ensure your data works for you. I also create workflow automations and integrations with tools like n8n, CrewAI, Zapier, Make, Twilio, Jotform, Google Sheets, CRMs, and APIs, eliminating repetitive tasks and boosting operational efficiency. My AI solutions leverage LLMs (GPT, Claude, etc.) to automate replies, intelligent routing, and decision-making workflows, making your operations smarter and faster. I deliver projects quickly (24–48 hours), provide clear documentation, and record walkthroughs for immediate adoption. Here are the five case studies of Agentic AI & Data Engineering Solutions I have implemented for clients: 1. AI-Powered Anomaly Detection for ETL Pipelines Developed an AWS Lambda function that integrates with Amazon Bedrock (Anthropic Claude) to enhance data quality checks. Key Highlights: * Extracts ETL log tables from Amazon Redshift * Utilizes a Large Language Model to analyze daily data loads * Detects anomalies in record counts and aggregate metric values 2. Agentic RAG Solution for Enterprise Knowledge Management Configured a Retrieval-Augmented Generation (RAG) solution using Amazon Q Business, integrating Confluence, Jira, S3, and SharePoint. Key Highlights: * Federated login implemented via Okta identity * Unified access to internal documentation and data for AI-powered responses * Enables automated knowledge retrieval and decision-making workflows 3. AI-Powered Document Verification System for Fintech Built a serverless, AI-powered document verification system that automates fraud detection at the point of upload. Key Highlights: * Flags duplicate or suspicious loan documents * Detects missing or incorrect supporting certificates * Event-driven pipeline triggers instantly on uploads to Amazon S3 using AWS Lambda, Amazon Textract, and Terraform 4. Scalable, Resilient Data Platform on AWS for Fintech Designed and delivered a fully containerized, event-driven AWS-native data platform to unify multiple data sources. Key Highlights: * Export process: Python extractors in Docker containers via AWS Fargate, data landed in S3, logs in Redshift * Import process: Containerized Python jobs moved validated data to Redshift staging and Lake layers * Data quality: Automated checks using Great Expectations * Transformations: Modular dbt transformations with CI/CD * Infrastructure and orchestration: Terraform, Apache Airflow, monitoring via Slack and Outlook 5. Data Platform Modernization on Azure Modernized data platforms and analytics workflows using Azure Data Services and Databricks. Key Highlights: * Migrated datasets from Google BigQuery to Databricks * Built ADF pipelines for SAP, Oracle, and flat files * Developed Spark code for batch and streaming data from Azure EventHub * Supported CI/CD setup for ADF and Databricks * Conducted data reconciliation between old BigQuery and new Databricks datasets * Developed POCs for Microsoft Fabric and assessed performance and scalability Clients worldwide rely on my solutions to save time, scale efficiently, and focus on high-value work. If you want a proven expert in AI, automation, and data engineering to create actionable, high-impact solutions for your business, I am ready to help.