Professional Overview
AI Engineer with hands-on experience building production-ready AI applications using Python, FastAPI, LLMs, RAG, LangGraph, and Computer Vision. Developed scalable AI workflows, semantic search systems, and multi-agent solutions during my AI internship. Passionate about building intelligent products that solve real-world business problems.
Experience
AI Expertise
Skills
Employment Type
Description of Responsibilities
- Built production-grade RAG pipelines for enterprise document search.
- Developed multi-agent AI workflows using LangGraph and NVIDIA NemoClaw.
- Built FastAPI REST APIs for AI-powered applications.
- Integrated vector databases for semantic search and retrieval.
- Deployed and maintained scalable AI services using Python and Docker.
Key Achievements / KPIs
- Deployed and maintained scalable AI services using Python and Docker.
- Delivered production-ready AI solutions for enterprise workflows.
- Built scalable semantic search across enterprise documents.
- Contributed to end-to-end AI deployment using Python and Linux.
Education
Field of Study: Computer Science and Engineering (Artificial Intelligence & Machine Learning)
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.
NLP & Prompt Engineering
Build natural language processing systems, fine-tune large language models, and craft reliable production prompts for AI-powered products.
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.
