shreshth@home — STATUS: ONLINE
Shreshth Rai
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AI Researcher and Applied ML Engineer
AI Researcher and ML Engineer building multimodal systems and robust model architectures for real-world deployment.
Technical Stack
// CORE ARSENAL
Core ML / AI
PyTorchTensorFlowTransformersPEFTQuantizationDistillation
Multimodal & CV
AEs/VAEsGANsVision TransformersDiffusion ModelsSelf-Supervised LearningVision Language Models
Backend / Systems
FastAPINode.jsPostgreSQLMongoDBRedis
Programming
PythonCC++JavaScriptSQLBash
Data Science
NumPyPandasScikit-learnMatplotlibSeabornFAISS
Frontend
HTMLCSSReactNext.jsTypeScript
Cloud/Infra
AWSGCPAzureDockerKubernetesNginx
// WORKING KNOWLEDGE
Miscellaneous
ChromaDBJavaKubernetesKotlin
Selected Projects
DECIBELMM_MOE
Scaling unified audio-language models under strict compute and memory constraints while maintaining strong multimodal reasoning performance.
Designed a Mixture-of-Experts multimodal architecture with 7 specialized expert models. Applied parameter-efficient fine-tuning (LoRA, adapters), quantization, and distillation to reduce VRAM footprint (<6GB) while preserving performance. Integrated Wav2Vec2 embeddings and fine-tuned Falcon-7B for joint audio-text reasoning.
FashionateMM_GAN
Learning high-fidelity multimodal representations for personalized fashion recommendation and realistic virtual try-on.
Built a multimodal recommendation system using vision-language embeddings and large-scale similarity search. Implemented GAN-based virtual try-on with SPADE and DensePose for garment warping. Optimized inference pipelines and embedding efficiency for scalable retrieval and personalization.
Vision-SSLSSL_CV
Reducing dependence on labeled data for high-performance visual representation learning.
Implemented self-supervised learning pipelines using SimCLR and Masked Autoencoders (MAE) on ImageNet-100. Studied representation quality under limited supervision and optimized training efficiency through contrastive learning and masked modeling approaches.
Research Core
Inference Time Expert Subsets for Gradient Misaligned Robustness (Under Review)
Research in adversarial machine learning focused on improving robustness of deep models under worst-case attacks using ensembling and inference-time methods.
Professional Log
Orangecat Technologies — Artificial Intelligence InternJun 2026 — Present
Bitrix Innovations — Full Stack AI EngineerJan 2026 — Apr 2026
Engineering LLM-driven video synthesis pipelines. Optimized AWS deployment workflows resulting in 40% latency reduction. Leading technical integration of generative models into production environments.
Department of Applied Mathematics, DTU — Research InternMay 2025 — Jun 2025
Pioneered multimodal sentiment analysis using BiLSTM and Vision Transformers (ViTs). Focused on novel feature extraction from unstructured audio and visual data streams.
5G Research Lab, DTU — Research InternJun 2025 — Jul 2025
Built AI-driven applications using 5G-enabled vision systems. Developed facial recognition pipelines with Siamese Networks. Studied 5G SA core architecture and its integration with real-time edge inference and SDR systems.
Initiate Contact
Available for research-driven projects and AI engineering roles.