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AI Engineer with less than a year in ML model deployment & data pipeline engineering.
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Assessing your cultural and operational fit
Currently pursuing a B.Tech in Computer Science Engineering (Artificial Intelligence), I have 4 months of experience as an AI/ML Intern, where I focused on data preprocessing, building and evaluating classification and regression models, and deploying ML models as REST APIs. My skills include Python, SQL, various AI/ML/DL frameworks like TensorFlow and PyTorch, Generative AI tools such as LangChain and OpenAI API, and deployment platforms like FastAPI and AWS. I have successfully contributed to projects involving AI analytics agents, RAG chatbots, and heart disease prediction systems.
Marwadi University
B.Tech · Computer Science Engineering (Artificial Intelligence)
August 1, 2021 – June 30, 2025
Syncwave Automation Pvt. Ltd.
AI/ML Intern
July 1, 2024 – October 1, 2024
India
AI Business Analyst Agent
December 1, 2025 – February 1, 2026
• Built an end-to-end AI analytics platform using Gemini API that auto-generates KPI reports, 12-month sales forecasts, and strategic recommendations from raw business datasets, reducing manual reporting time by ~60%. • Designed a RAG pipeline with LangChain + FAISS vector database enabling semantic search over 10,000+ business document chunks with 90%+ retrieval accuracy. • Engineered a complete data pipeline - ingestion, embedding, indexing, and querying - integrated with FastAPI backend and PostgreSQL for persistent report management.
News Research Tool - RAG Chatbot
February 1, 2025 – April 1, 2025
• Built a production-style NLP-powered RAG chatbot ingesting news article URLs, indexing 5,000+ text embeddings in FAISS, delivering source-attributed answers with <2s response time. • Applied semantic chunking and embedding optimisation, improving retrieval precision by ~25% over baseline keyword search; deployed as a Streamlit web application.
Heart Disease Prediction System
March 1, 2024 – May 1, 2024
• Benchmarked 4 ML models (Logistic Regression, Random Forest, KNN, SVM) on UCI Heart Disease dataset; achieved best-in-class 89% accuracy with Random Forest after hyperparameter tuning. • Deployed model as a FastAPI REST endpoint handling real-time predictions with probability scores; documented API with Postman for integration testing.
AWS Cloud Practitioner
Amazon Web Services
June 1, 2026 – Present
AI for Everyone
Coursera (Andrew Ng)
June 1, 2026 – Present
Machine Learning with Python
IBM
June 1, 2026 – Present
Data Analytics Essentials
Cisco
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate initiative and a proactive approach to learning and applying cutting-edge AI technologies. The diversity of projects (AI Business Analyst, RAG Chatbot, Heart Disease Prediction) shows a broad interest in different AI applications. The internship experience aligns well with practical industry exposure. The certifications indicate a commitment to continuous learning and skill development, which are positive indicators for cultural fit in a dynamic AI engineering environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems, manage data pipelines, and deploy models, suggesting good problem-solving and execution skills. The internship experience further supports practical application of learned concepts. However, without direct assessment data on collaboration, stress handling, or communication in a team setting, a comprehensive evaluation of soft skills and operational fit is limited.