AI/ML Research Engineer and DevOps/MLOps Engineer focused on building scalable machine learning systems — from research prototypes to production deployments.

Currently working on cloud-native AI platforms and applied deep learning systems across industry and research environments.

• DevOps Engineer – HashedIn by Deloitte (Core platform team, AI Assist)
• Research Intern – Samsung R&D (Generative Vision, GANs)
• AI/ML Engineering Intern – DRDO (Computer Vision Systems)
• 2 Springer-indexed research publications

• Generative Models (GANs, Super-Resolution, Style Transfer)
• Retrieval-Augmented and Robust LLM Systems
• Applied Computer Vision for real-world deployment
• Scalable ML Systems & MLOps Infrastructure

A cloud-native ML inference platform deploying YOLO models at scale using Kubernetes and Terraform.
Includes autoscaling, caching, monitoring, CI/CD, and multi-cloud infrastructure.

→ Designed for production-grade, reproducible ML deployments

Reusable training and inference framework built for experimentation, evaluation, and rapid prototyping of vision models.

→ Focused on clean architecture and research-friendly workflows

Structured Relevance Assessment for Robust Retrieval-Augmented Language Models
Springer LNNS
Improved reliability of RAG pipelines through structured relevance evaluation and validation metrics.

Advanced Deep Learning Framework for Audio Forensics and Anomaly Detection
Springer CCIS
Designed Conv1D + BiLSTM architectures for forensic audio classification and anomaly detection.

Machine Learning: PyTorch, TensorFlow, GANs, CNNs, RAG
Cloud & MLOps: Docker, Kubernetes, Terraform, CI/CD, Redis
Backend: Python, FastAPI
Data & Tooling: OpenCV, NumPy, Pandas, Git, Linux

• Preparing for MS in Australia
• Open to Research Assistant and MLOps/DevOps Internship opportunities
• Building production-grade ML systems

Email: gargveerastitva@gmail.com
LinkedIn: https://linkedin.com/in/astitva-veer-garg