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AI Engineer with less than a year in Software Development and strong project experience in AI/ML & M
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Assessing your cultural and operational fit
Vraj Prajapati is an aspiring AI/ML Engineer with 4 months of experience as a Software Development Intern, focusing on scalable web portals, database optimization, and real-time systems. He possesses strong technical skills in Python, AI/ML (LLMs, RAG, LangChain), and MLOps, demonstrated through several end-to-end projects like a Multi-Agent Travel Planner, a Used Car Price Prediction MLOps Pipeline, and an AI-Driven Solar Inverter Failure Prediction Platform.
Nirma University
Master of Technology · Computer Science and Engineering
August 1, 2025 – Present
Birla Vishvakarma Mahavidyalaya
Bachelor of Technology · Computer Engineering
August 1, 2021 – June 30, 2025
Dhavat Infotech Pvt. Ltd.
Software Development Intern
January 1, 2025 – April 1, 2025
Gujarat, India
Multi-Agent Agentic Travel Planner for Indian Destinations
June 25, 2026 – Present
Designed a multi-agent blackboard architecture with 6 specialized agents (Discovery, Weather, Budget, Safety, XAI, Planner) coordinating through a shared state for end-to-end travel planning. Built a full RAG pipeline using SentenceTransformers and FAISS for semantic retrieval, re-ranking, and context-aware LLM-based itinerary generation. Integrated real-time weather data via Open-Meteo API and implemented an Explainable AI agent providing transparent, human-readable destination rankings.
Used Car Price Prediction – End-to-End MLOps Pipeline
June 25, 2026 – Present
Built an end-to-end ML pipeline with automated feature engineering, hyperparameter tuning via GridSearchCV, achieving around 94% accuracy with XGBoost. Developed FastAPI service with JWT authentication, MLflow integration for experiment tracking, model versioning, and automated retraining. Deployed using Docker Compose with Jenkins CI/CD and Prometheus-Grafana monitoring for performance tracking.
AI-Driven Solar Inverter Failure Prediction Platform
June 25, 2026 – Present
Built a predictive maintenance platform to forecast inverter failures using ensemble ML models with automated F1-score-based model selection. Designed microservices architecture using FastAPI, Kafka, Redis, and MongoDB for real-time data processing and ML inference. Integrated SHAP for model explainability and a retrieval-augmented system for human-readable failure diagnostics.
Qualified GATE 2025 - Data Science & Artificial Intelligence
Unknown
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest and practical application in AI/ML, which aligns well with an AI Engineer role. The diversity of projects, from predictive maintenance to multi-agent systems and MLOps, shows a broad technical curiosity and willingness to tackle different challenges. The internship experience in software development also indicates an understanding of production-grade systems. The candidate's academic background and achievements (GATE qualification) suggest a driven and academically strong individual. The focus on Python, ML frameworks, and MLOps tools indicates a good fit for a modern AI engineering culture.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive approach to problem-solving and a strong understanding of system design principles. The multi-agent travel planner project suggests an ability to break down complex problems into manageable components and integrate diverse functionalities. The MLOps pipeline project highlights an understanding of operational efficiency and monitoring. However, without direct interview data, specific soft skills like teamwork, leadership, or conflict resolution cannot be definitively assessed.