AI Engineer with less than a year in Machine Learning & Data Pipelines
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AI Lead at Google Developers Group on Campus MCKVIE, specializing in AI & Machine Learning, Data & Orchestration, and Systems & Infrastructure. Proficient in Python, PyTorch, TensorFlow, and experienced in building complex ML systems for geospatial data and edge-AI applications through impactful engineering projects and competitive hackathons.
MCKV Institute of Engineering
B.Tech · Computer Science & Engineering
N/A – June 30, 2027
Project Kishan - Geospatial ML & Multimodal RAG Engine
June 26, 2026 – Present
Orchestrated an automated satellite pipeline using Google Earth Engine to extract dynamic spectral indices (NDVI, NDWI, NASA SMAP), enforcing strict geographic guardrails via ESA WorldCover v200 to reject non-vegetative mapping and save compute resources. Engineered a localized Retrieval-Augmented Generation (RAG) system utilizing LangChain and HuggingFace to index 11 ICAR agricultural PDFs into a local FAISS vector database. Optimized retrieval using 384-dimensional embeddings and a 500-token chunking strategy with 50-token overlap to preserve inter-chunk context. Integrated a Flask backend with Twilio, enabling a multilingual WhatsApp interface.
View ProjectActive Radar - Edge-AI Sensor Telemetry Node
June 26, 2026 – Present
Built an end-to-end PySerial framework in Python to ingest, clean, and buffer 30 seconds of high-frequency chaotic analog signals from a custom active hardware gimbal at a 115200 baud rate, dynamically mitigating structural noise across spatial, optical, and thermal telemetry. Designed and trained a lightweight 1D-Convolutional Neural Network (16 filters, 8 dense layers, TensorFlow) to decode latent mathematical correlations within noisy hardware streams, accurately classifying physical states without relying on computationally expensive or privacy invasive camera feeds. Engineered a low-latency Flask web backend to serve live neural network classifications directly from the hardware edge.
View ProjectAl Lead
Google Developers Group on Campus MCKVIE
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong alignment with an AI Engineer role, showcasing practical application of machine learning, data engineering, and deployment. The academic projects are diverse, covering geospatial analysis, RAG systems, and edge AI, indicating a broad interest and capability in different AI domains. The 'AI Lead' certification from Google Developers Group on Campus further reinforces a proactive engagement with the AI community. However, the lack of professional experience and focus solely on academic projects might indicate a need for mentorship in a corporate environment.
Soft Skills & Operational Fit
The candidate's project descriptions indicate strong problem-solving skills and an ability to tackle complex technical challenges. The involvement in hackathons suggests a proactive and competitive attitude. However, without specific psychometric or English test scores, a comprehensive assessment of communication, teamwork, and stress handling is not possible. The detailed project descriptions suggest good technical communication in writing.