Professional Overview
AI Engineer & Data Platform Engineer with 4+ years of experience delivering production-grade solutions across data engineering, machine learning, and full-stack systems. PhD candidate in Computer Science with publications in deep learning and computer vision. Proven expertise in designing scalable data platforms, building end-to-end ML pipelines.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Designed Microsoft Fabric data platforms and Lakehouse architectures.
- Built ETL/ELT, analytics, and ML pipelines with PySpark and Airflow.
- Developed predictive models, AutoML solutions, and Power BI dashboards.
- Engineered scalable data workflows and optimized PostgreSQL databases.
- Implemented data quality, orchestration, and cloud analytics solutions.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Designed AI pipelines using LLMs and computer vision.
- Automated data collection and labeling with web scraping.
- Managed asynchronous background tasks with Celery.
- Developed scalable APIs for AI applications.
- Built real-time analytics dashboards and reporting.
Key Achievements / KPIs
- Delivered production-ready AI pipelines for image classification.
- Automated data acquisition, improving dataset quality and reliability.
- Reduced user wait times through asynchronous PDF generation.
- Built scalable APIs supporting high-performance AI services.
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built full-stack analytics platforms with React, Django, and PostgreSQL.
- Developed optimized ETL pipelines for financial data.
- Created executive dashboards using Power BI.
Key Achievements / KPIs
- Delivered end-to-end analytics platforms for business reporting.
- Improved ETL performance for high-volume financial datasets.
- Enabled executive decision-making with interactive Power BI dashboards.
Areas of Expertise
AI & Machine Learning Engineers
Design, train, and ship production ML models — from data pipelines to model inference at scale. Covers supervised learning, neural networks, LLM integration, and model optimisation.
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
