Welcome to my AI & Data Engineering Portfolio!
This repository showcases my expertise in Agentic AI, Machine Learning, Workflow Automation, Healthcare IT, and Data Analytics.
With 9+ years of IT experience (including 5+ years in AI & automation), I’ve worked on projects that span across healthcare, retail, and enterprise environments — always with a focus on building scalable, compliant, and impactful solutions.
Objective: Automate anomaly detection in radiology workflows.
- Approach:
- Designed an AI agent using LangChain + n8n.
- Integrated with PACS/RIS data streams.
- Triggered automated alerts when imaging backlogs or SLA breaches occurred.
- Tools Used: LangChain, LangGraph, n8n, DICOM, Python
- Outcome: Reduced manual intervention by 40%, accelerated clinical escalations, and improved operational efficiency.
Objective: Provide clinicians and staff with a secure, intelligent Q&A agent.
- Approach:
- Implemented Retrieval-Augmented Generation (RAG) with Pinecone.
- Fine-tuned LLMs with GDPR-compliant healthcare datasets.
- Deployed via FastAPI for seamless system integration.
- Tools Used: Pinecone, LangChain, FastAPI, Python
- Outcome: Cut down query resolution time by 50% while ensuring compliance.
Objective: Identify operational anomalies in radiology processes.
- Approach:
- Built an Isolation Forest model in Python.
- Ingested imaging system logs in real-time.
- Deployed via Docker for scalable integration.
- Tools Used: Python, Scikit-learn, Isolation Forest, Docker
- Outcome: Reduced imaging workflow errors by 25%.
Objective: Forecast patient outcomes to improve hospital resource planning.
- Approach:
- Built predictive models with XGBoost.
- Integrated into hospital systems using FastAPI APIs.
- Visualized predictions on clinician dashboards.
- Tools Used: Python, XGBoost, FastAPI, SQL, Power BI
- Outcome: Improved resource allocation accuracy by 20%.
Objective: Segment patients/customers for targeted healthcare services.
- Approach:
- Applied K-Means clustering to patient demographics & behavior data.
- Visualized clusters in Power BI dashboards.
- Tools Used: Python, Scikit-learn, Power BI
- Outcome: Improved patient engagement by 15%.
Objective: Provide real-time visibility into imaging department performance.
- Approach:
- Connected PACS/RIS and EHR data to Power BI.
- Built dashboards for appointment volumes, backlog, SLA breaches, referral trends.
- Tools Used: Power BI, SQL, PACS/EHR data
- Outcome: Reduced reporting time by 30%, enabling data-driven management decisions.
Objective: Track revenue and performance across business units.
- Approach:
- Modeled transactional data into Power BI.
- Created KPIs for revenue, profit, growth.
- Built regional and product-level drill-downs.
- Tools Used: Power BI, SQL, DAX
- Outcome: Helped management identify underperforming regions and products.
Objective: Automate ingestion and transformation of healthcare datasets.
- Approach:
- Built ETL workflows using Airflow + Python + SQL.
- Handled patient demographics, imaging metadata, and hospital records.
- Optimized for efficiency and compliance with GDPR standards.
- Tools Used: Airflow, Python, SQL
- Outcome: Improved data processing efficiency by 35% and ensured reliable analytics.
Objective: Automate anomaly detection from IT system logs.
- Approach:
- Preprocessed large-scale text logs using NLP pipelines.
- Extracted entities and key patterns for anomaly detection.
- Visualized log insights in dashboards.
- Tools Used: Python, NLP, Pandas, Matplotlib
- Outcome: Reduced manual log review by 95%, saving significant operational time.
Objective: Design a structured data warehouse for NHS reporting.
- Approach:
- Created normalized schemas for healthcare data.
- Wrote optimized SQL queries for KPI reporting.
- Ensured HL7/DICOM compliance in data flows.
- Tools Used: SQL Server, PostgreSQL
- Outcome: Accelerated compliance reporting and improved query performance.
Objective: Evaluate staff productivity and retention.
- Approach:
- Wrote SQL queries to measure performance trends.
- Designed queries for employee segmentation (top performers, risk of attrition).
- Tools Used: SQL, Power BI
- Outcome: Provided actionable insights for HR strategy.
Objective: Visualize engagement trends across demographics and user roles.
- Approach:
- Built an interactive Tableau dashboard with filters for role, team, and demographics.
- Integrated engagement history for trend analysis.
- Tools Used: Tableau, Excel, SQL
- Outcome: Enabled management to tailor services for improved engagement.
- Languages: Python, SQL
- AI & ML: LangChain, LangGraph, Scikit-learn, XGBoost, NLP, Isolation Forest
- Automation & Orchestration: Airflow, n8n, Zapier
- Visualization: Power BI, Tableau, Looker
- Databases: Pinecone, Weaviate, PostgreSQL, MySQL
- Healthcare IT: HL7, DICOM, PACS/RIS, NHS Digital Standards, GDPR Compliance
- DevOps & Others: Docker, Git, FastAPI
1️⃣ Browse through folders for:
/AI_Agents/→ Agentic AI & automation projects/ML_Projects/→ Machine learning case studies/PowerBI/→ Power BI dashboards/Python_ETL/→ Python & Airflow ETL workflows/SQL/→ SQL analysis & scripts/Tableau/→ Tableau dashboards
2️⃣ Each folder contains:
- 📌 Project files (code, notebooks, dashboards)
- 📌 Documentation & datasets (if shareable)
- 📌 README files with step-by-step project details
📧 Email: [email protected]
💼 LinkedIn: Hitendrasinh Rathod
🚀 Let’s collaborate on AI-powered, data-driven, and healthcare-compliant solutions! 🚀