AI Engineer with less than a year in Generative AI, MLOps, and Agentic Systems
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Assessing your cultural and operational fit
Highly motivated and skilled AI Engineer pursuing a Bachelor of Technology in Computer Science with a strong focus on Machine Learning, Generative AI, and Agentic Systems. Proven ability to design and implement complex AI models, develop RAG agents, and contribute significantly to open-source projects like Keras. Eager to leverage deep learning expertise and problem-solving skills in innovative technical environments.
Indian Institute of Information Technology Vadodara-ICD
Bachelor of Technology · Computer Science
August 1, 2024 – June 30, 2026
Google Open Source
Open Source Contributor
June 1, 2026 – Present
India
IIITV-ICD
Technical Committee Member
May 1, 2026 – Present
India
CAS - Codebase Agent
January 1, 2026 – Present
• Built a system that enables developers to onboard 10x faster by instantly surfacing architecture patterns, dependencies, and code intent by turning any unfamiliar codebase into a queryable knowledge base. • Developed a Retrieval-Augmented Generation (RAG) agent to ingest, index, and query complex codebases. • Engineered an automated ingestion pipeline using vector embeddings to store external repositories in ChromaDB. • Built a FastAPI interface for AI-driven code efficiency suggestions and project architecture identification.
View ProjectKeystroke Biometrics Model
December 1, 2025 – December 1, 2025
• Engineered a keystroke biometrics model for continuous behavioral authentication, capturing timing dynamics to passively verify user identity without passwords or hardware tokens.
Cloud Cleaning and Enhancement Model
October 1, 2025 – Present
• Designed a model which delivers cloud-free satellite imagery through a fully automated 3-stage pipeline, enabling industries like agriculture, urban planning, and disaster response to make high-confidence geospatial decisions without weather delays. • Designed a custom ResNet-18 architecture for precise cloud segmentation, achieving 94% accuracy. • Built a dual-stage pipeline with Gated Partial Convolutions for de-clouding, improving SSIM by 27%. • Enhanced visual resolution using RRDB (ESRGAN) upscaling to restore sharp satellite imagery details.
View ProjectEmotion Recognition using Spectrograms
August 1, 2025 – Present
• Engineered a real-time emotion recognition layer for applications in mental health monitoring, customer experience, and human-computer interaction by predicting human emotions from raw audio without relying on facial data or wearables. • Developed a EfficientNetV2B0 + BiGRU model achieving 73% validation accuracy across six emotion classes. • Processed 12,206 labeled samples into Mel-spectrograms for deep learning-based recognition. • Evaluated multiple architectures including LightCNN and MobileNetV2, optimizing for lightweight performance.
View ProjectHackathon Finalist (Top 10)
HackNagpur 2.0 (National-level Hackathon by GDG)
December 1, 2025 – Present
Cultural Fit Analysis
The candidate's project diversity, ranging from RAG agents to computer vision and audio processing, indicates a broad interest in AI applications. Their open-source contributions to Keras show a willingness to engage with the wider technical community and contribute to large-scale projects. Participation in a national-level hackathon and a technical committee role further demonstrate initiative and collaborative spirit. The academic focus of their experience (still pursuing a bachelor's degree) suggests a strong theoretical foundation and a drive for continuous learning, which is beneficial for a rapidly evolving field like AI. However, the lack of professional industry experience beyond academic projects and open-source contributions might require some adjustment to corporate operational rhythms.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through open-source contributions and hackathon participation. Their involvement in a technical committee suggests teamwork and collaboration abilities. The project descriptions are clear and highlight technical achievements, indicating good communication of complex ideas. The academic nature of projects suggests a learning-oriented and research-driven approach, which aligns well with innovative AI roles.