Professional Overview
I'm a Software Engineer with 4+ years of experience building and supporting production systems in Python, with a strong focus on debugging, automation, and workflow tooling. I also bring hands-on data analytics experience (Pandas, Power BI) and use AI tools like Copilot and Claude to ship cleaner code faster.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built and maintained features in Python for a real-time visualization platform
- debugging and resolving critical system issues.
- Automated internal workflows and data pipelines using Python
- Used Pandas, NumPy, and SQL to clean, analyze, and structure data
- Collaborated in Agile/Scrum teams and used CI/CD pipeline
Key Achievements / KPIs
- Cut manual troubleshooting time by automating workflows with Python scripts
- Mentored new engineers, speeding up onboarding and project ramp-up
- Maintained stability of licensing modules across releases
- Streamlined release pipeline using CI/CD, reducing deployment effort
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built and maintained features in Python for a real-time visualization platform
- debugging and resolving critical system issues.
- Automated internal workflows and data pipelines using Python
- Used Pandas, NumPy, and SQL to clean, analyze, and structure data
- Collaborated in Agile/Scrum teams and used CI/CD pipeline
Key Achievements / KPIs
- Cut manual troubleshooting time by automating workflows with Python scripts
- Mentored new engineers, speeding up onboarding and project ramp-up
- Maintained stability of licensing modules across releases
- Streamlined release pipeline using CI/CD, reducing deployment effort
Education
Field of Study: Software engineering
Areas of Expertise
Data Science & Analytics
Extract business insights from raw data using statistical modelling, predictive analytics, A/B testing, and interactive dashboards.
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.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
