I’m an AI/ML Engineer with 16+ years of experience across security, backend development, data engineering, and DevOps.
Currently, I focus on designing and deploying production-grade AI systems using deep learning, NLP, and large language models (LLMs). My technical strengths include:
- Neural Networks (ANN,CNN,RNN)
- Transformer Architecture
- Distributed training
- MLOps using PyTorch, Docker, and Kubernetes
- Cloud Platforms GCP,AWS,Azure
- CUDA GEMM optimizations
I deliver high-performance, cloud-native solutions across platforms like GCP, AWS, and Azure.
My career began as an SAP Security Consultant and has evolved into a full-stack ML practitioner role. This journey uniquely equips me to design and operationalize end-to-end machine learning pipelines.
I’m also expanding my expertise in Deep Learning & ML through Scaler’s Data Science Program, while leading strategic AI initiatives that align closely with enterprise needs.
Full-Stack LLM Systems | From Data to Deployment
My background spans multiple areas critical to LLM development, and with my current LLM expertise, I can bring them together to build and optimize the full lifecycle.
- Data Engineering: Scalable pipelines, tokenization, dataset curation.
- Model Design: Transformers, Attention Mechanisms, Positional Encoding.
- Pre-Training: Distributed training with mixed precision, DeepSpeed, FSDP.
- Fine-Tuning: Efficient adaptation with LoRA, QLoRA, quantization.
- Continual Learning: Retraining models with new data and domain adaptation.
Current Deep-Dive Topics
- Transformer Architectures
- Distributed Training at Scale
- GPU Programming and Optimization