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AI Engineer with less than a year in building Graph RAG pipelines & FastAPI orchestration.
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Assessing your cultural and operational fit
AI Engineer with production experience building Graph RAG pipelines, LangChain agentic workflows, and FastAPI orchestration systems, achieving zero deployment failures and elimination of manual scheduling overhead. Proficient in LangChain, Neo4j, FastAPI, TensorFlow, and Scikit-learn.
St. Joseph's College, Tiruchirappalli
M.Sc. · Data Science
June 1, 2024 – April 1, 2026
Holy Cross College, Tiruchirappalli
B.Sc. · Mathematics
June 1, 2021 – April 1, 2024
Xyloite Technologies
AI Engineer Intern
December 1, 2025 – February 1, 2026
Coimbatore, Tamil Nadu, India
Customer Segmentation Model
June 1, 2025 – June 1, 2026
Trained KMeans clustering on multi-feature behavioural data with PCA dimensionality reduction and silhouette scoring; deployed real-time segment predictions via an interactive Gradio interface.
Generative AI Application
June 1, 2025 – June 1, 2026
Built a chained LLM application using structured prompt templates and LangChain sequential chains; deployed as a Flask web app demonstrating end-to-end LLM pipeline design with Google Gemini integration.
Graph RAG Document Intelligence Pipeline
June 1, 2025 – June 1, 2026
Built a knowledge graph-powered RAG system for structured data extraction from unstructured documents, combining vector similarity search with multi-hop graph traversal for context-rich LLM responses.
View ProjectNLP Data Preparation Pipeline
June 1, 2025 – June 1, 2026
Developed an end-to-end NLP pipeline covering web scraping, tokenization, lemmatization, stopword removal, and multi-class text annotation, processed 1000 samples for document classification workflows.
View ProjectCultural Fit Analysis
The candidate's projects demonstrate a strong interest and initiative in various AI domains, including NLP, generative AI, and traditional ML. The personal projects complement the internship experience, showing a proactive learning attitude. The focus on practical applications and deployment aligns well with a results-oriented culture. The breadth of skills across LLMs, ML, DL, NLP, and databases indicates adaptability and a willingness to explore different technical areas. However, the experience is primarily academic and internship-based, so the extent of collaboration in a larger team or corporate environment is less clear.
Soft Skills & Operational Fit
The candidate's project descriptions and internship experience highlight an ability to work on end-to-end solutions, from data preparation to deployment and monitoring. The focus on reducing manual tasks and ensuring zero production failures suggests a detail-oriented and operationally aware mindset. The description of refining context windows and few-shot examples indicates an iterative and problem-solving approach. However, without direct interview data, specific soft skills like teamwork, leadership, or stress handling cannot be definitively assessed.