AI Engineer with less than a year in Large Language Models & Agentic AI Systems
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Assessing your cultural and operational fit
AI Engineer specializing in Large Language Models (LLMs), Agentic AI Systems, Multi-Agent Workflows, Retrieval-Augmented Generation (RAG), and Machine Learning. Experienced in designing AI agents, semantic retrieval systems, enterprise AI automation solutions, and production-grade ML pipelines using Python, PyTorch, FAISS, and modern AI frameworks.
Delhi Technological University
M.Tech · Computer Science Engineering
August 1, 2024 – June 30, 2026
Sinhgad College of Engineering, Pune
B.E. · Computer Science Engineering
August 1, 2018 – June 30, 2022
STMicroelectronics
AI Project Trainee
July 1, 2025 – Present
Noida, Uttar Pradesh, India
Emotion-Aware Music Recommendation System
June 1, 2026 – Present
• Developed a CNN-based facial emotion recognition model for real-time affect detection from webcam inputs. • Built an emotion-driven recommendation engine that maps user sentiment to personalized music playlists. • Implemented image preprocessing, feature extraction, and deep learning workflows achieving 90%+ emotion classification accuracy. • Integrated computer vision and recommendation pipelines to deliver an adaptive and personalized user experience.
View ProjectLocal RAG System | Enterprise Document Intelligence
June 1, 2026 – Present
• Built an end-to-end Retrieval-Augmented Generation (RAG) system using FAISS, Sentence Transformers, and local LLMs for technical document question answering. • Implemented semantic chunking, vector indexing, query expansion, and similarity-based retrieval pipelines across large technical document collections. • Developed context-aware prompting and grounded answer generation to reduce hallucinations and improve response quality. • Designed a fully local inference architecture enabling secure document intelligence without reliance on external APIs.
View ProjectVoice Finance Buddy | Multi-Agent Financial AI Assistant
June 1, 2026 – Present
• Architected a multi-agent financial assistant using GPT-4, Whisper, Retrieval-Augmented Generation (RAG), and persistent memory for multilingual (Hindi/English/Hinglish) interactions. • Designed intent-based routing and function-calling workflows across Budget, Expense, Investment, and Tax specialist agents. • Implemented conversation memory, financial knowledge retrieval, and guardrail mechanisms for context-aware and secure responses. • Developed an end-to-end voice AI pipeline (Speech-to-Text → Agent Reasoning → Tool Execution → Text-to-Speech) achieving sub-3 second response latency.
View ProjectAI-Powered Inventory & Demand Forecasting System
June 1, 2026 – Present
• Developed a demand forecasting platform using Prophet, Pandas, and NumPy for inventory planning and future demand prediction. • Implemented inventory optimization algorithms including Economic Order Quantity (EOQ) and Reorder Point (ROP) calculations for supply-chain decision support. • Built automated forecasting pipelines incorporating feature engineering, preprocessing, and real-time prediction workflows. • Developed an interactive Streamlit dashboard for inventory monitoring, forecasting visualization, and operational analytics.
View ProjectRoad Pothole Detection System
June 1, 2026 – Present
• Developed a real-time road defect detection system using YOLOv4 for automated pothole identification from images and video streams. • Built data preprocessing, model training, and inference pipelines achieving high-accuracy object detection performance. • Optimized inference workflows for low-latency processing and deployment-ready computer vision applications. • Developed monitoring and visualization components for rapid road-condition assessment and infrastructure inspection.
View ProjectMicrosoft Azure AI Essentials Professional Certificate
Microsoft
June 1, 2026 – Present
Career Essentials in Generative AI
Microsoft and LinkedIn
June 1, 2026 – Present
Career Essentials in GitHub Professional Certificate
GitHub
June 1, 2026 – Present
The candidate scored 94% on the 'Data Scientist — Artificial Intelligence' test, indicating a strong grasp of the subject matter and excellent problem-solving abilities within this domain.
Strengths
Limitations
Cultural Fit Analysis
The candidate's diverse range of personal projects, from emotion-aware music recommendations to multi-agent financial assistants and pothole detection, demonstrates a broad interest in applying AI across various domains. The current internship at STMicroelectronics focusing on PLM automation and predictive analytics aligns well with enterprise AI applications. This breadth of experience and continuous learning through certifications (Azure AI, Generative AI) indicates a proactive and adaptable individual, suggesting a good cultural fit for an innovative AI engineering role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a strong ability to translate complex technical requirements into functional AI solutions. The detailed descriptions of multi-agent systems, RAG implementations, and end-to-end pipelines suggest good problem decomposition and architectural thinking. The focus on real-time systems and optimized workflows implies an understanding of operational efficiency. The psychometric test score of 321/500 suggests average performance in areas like logical reasoning, work attitude, stress handling, and team collaboration, which could be an area for further exploration during interviews.