Machine Learning Engineer · MLOps Builder
Aditya Raut
A scholar of intelligent systems
I forge models from raw data, temper them through quantization and deployment, and measure their worth in production where theory meets reality.
GGSC
Technical Lead
GenAI
Intern
The Scholar's Path
As a Generative AI Engineer at AllCognix, I forge RAG pipelines and LLM systems that breathe life into production environments. My craft spans the full ML lifecycle—from training ConvNeXt-Tiny disease classifiers with ONNX quantization to serving LightGBM models on 55 million rows through FastAPI and Docker vessels.
I currently delve deeper into the arcane arts of MLOps (MLflow, Evidently, Prefect) and the emerging realm of AI Agents, seeking to master the orchestration of intelligent systems that learn, adapt, and evolve.
The Quest
Seeking ML Engineer & GenAI Apprenticeships Across Realms
From the bazaars of India to distant shores, I seek opportunities where artificial intelligence meets human ingenuity.
Experience
AllCognix AI
Generative AI Engineer Intern
- Orchestrated a modern RAG pipeline migration from Verba to Haystack 2.x, reducing response latency by 40% and token consumption by 60% using GPT-4o-mini.
- Engineered an 8× accelerated document ingestion pipeline via parallelized processing (ThreadPoolExecutor), enabling scalable knowledge indexing.
- Architected multi-tenant retrieval systems using Weaviate metadata filtering, ensuring strict data isolation across users.
AI4Chat
Full-Stack Developer Intern
- Forged a multimodal AI system integrating real-time web search, LLM-driven reasoning, and dynamic response synthesis across text, image, and document modalities.
- Designed backend workflows for intelligent query routing, enabling context-aware decisions between search, generation, and analytical pipelines.
Project
Scientific Contributions
Ongoing Research · Agricultural Edge AI
Framework-Dependent Quantization Stability in Agricultural Edge AI
A systematic study on model stability under resource-constrained deployment. This work audits dataset integrity and challenges the architectural "fragility" of lightweight backbones by demonstrating the critical role of calibration strategies in quantized inference.
I. The 11.6% Leakage Audit
Utilizing pHash and MD5 verification to identify cross-split contamination in a 14,154-image wheat dataset, establishing a new "Clean" baseline for agricultural computer vision.
II. Quantization Backend Sensitivity
Demonstrating how Entropy-Calibrated Static Quantization (TensorRT) restores MobileNetV3 accuracy from 31% back to 82.5%, bypassing CPU-based dynamic limitations.
Metric Innovation: Deployment Efficiency Score (DES)
"Accuracy × ln(FPS - 1)" — A multi-objective success criterion designed to prioritize fluid control rates in autonomous field robotics.
Skills
Programming
- Python
- Java
- JavaScript
ML Systems / Backend
- FastAPI, Flask
- OpenAI/Anthropic API
- Git, Linux, PostgreSQL
MLOps & Deployment
- Docker, ONNX
- Apache Kafka
- Git LFS, Render, DockerHub
- CI/CD
ML / DL
- PyTorch, CNNs
- Transfer Learning
- ONNX Runtime
- Scikit-learn, XGBoost
- LightGBM, Hugging Face
- Fine-tuning
Achievements
2nd Place — IIC Udaan 2.0
April 2025
Technical Lead — Google Gemini Student Club
2025 – Present
The Scholar's Cabinet
Mechanical Artistry
Enchanted by the symphony of steel and speed in MV Agusta and Ducati craftsmanship. The De Tomaso P72 stands as modern sculpture in motion.
Celestial Navigation
Drawn to the ballet of combat systems and the dominion of the skies. The SR-71 Blackbird remains a testament to human audacity.
Cosmic Inquiry
Gazing into the firmament with my Phoenix 60700, seeking the infinite. The rings of Saturn whisper secrets of cosmic architecture.
Temple of Form
Honoring the classical ideal through the discipline of bodybuilding. Where physical excellence mirrors the pursuit of mental clarity.