Professional Overview
I build data systems that don't fall apart when things get messy pipelines, MDM workflows, and ML-ready data foundations. Curious about the gap between a working prototype and something production-ready. IEEE & Springer published. Snowflake certified. Open to ML Engineer, MLOps, and Data Engineering roles globally.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Designed and maintained MDM workflows in TIBCO EBX across 2+ business domains
- Enforced data governance policies in to standardize data quality
- Aligned master data standards to improve consistency of data for workflows.
Education
Field of Study: Software Engineering, Data Science, Machine Learning, Analytics, Statistics
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
