Professional Overview
I build AI automation systems and agentic workflows that actually ship to production. My background is cloud infrastructure and server administration — which means when I build an AI system, I don't just make it work on my laptop. I make it observable, deployable, and production-ready from day one. Right now I'm focused on: → Multi-agent systems
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Automated server monitoring and alerting pipelines using Python scripts
- Built scripts for backups, health checks, and user provisioning tasks
- Managed Linux/Windows servers across cloud and on-premise environments
- Reduced manual ops overhead by automating repetitive infrastructure work
- Maintained production uptime and reliability for client-facing systems
Key Achievements / KPIs
- Scored 95/100 in Cloud & Python Automation assessment during internship
- Secured Pre-Placement Offer (PPO) based on internship performance
- Demonstrated hands-on skills in cloud infrastructure and automation scripting
- Recognized for technical excellence leading to full-time employment offer
Education
Field of Study: Artificial Intelligence and Machine Learning
Field of Study: Software Engineering
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.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
