Professional Overview
I'm an AI Engineer specializing in LLMs, automation, and scalable systems. I help companies design, build, and deploy production-ready AI systems at scale using Python, LangChain, OpenAPI, etc. My specialization also spans LLM interpretability and AI agents evaluation and safety.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Designed and built an open-source agentic evaluation framework (RecruitBase).
- Architected the AI cloud infrastructure to enable AI agents to run in the cloud.
- Built an enterprise-grade CLI terminal for autonomous agentic evaluation.
- Configured the Azure cloud virtual machines to run agents in sandboxes.
Key Achievements / KPIs
- Achieved 96% accuracies on agentic evaluations
- Achieved a 99.9% system uptime for the enterprise dashboard of RecruitBase.
- Published the framework on Github, X, and Reddit and got initial traction.
Education
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.
MLOps & AI Infrastructure
Architect scalable ML infrastructure — CI/CD for ML, model versioning, experiment tracking, monitoring, and cloud deployment on AWS, GCP, or Azure.
Automation & AI Workflows
Build intelligent automation pipelines, integrate AI APIs into existing tools, and replace manual workflows with agentic AI systems.
AI Product & Strategy
Translate AI capabilities into product roadmaps, define success metrics, and drive adoption across engineering and business teams.
