Architected and deployed multi-tenant LLM endpoints using SageMaker LMI containers with VLLM backend and PEFT PPO/GRPO fine-tuning pipelines via Hugging Face PyTorch DLCs, achieving 70% memory reduction and 4x throughput increase for concurrent fine-tuned models serving 10+ tenants.

Deployed scalable multi-tenant serving architecture with SageMaker multi-model endpoints with dynamic batching, supports loading isolated LoRA adapters for enterprise clients and improving resource utilization by 3x.

Implemented end-to-end pipelines enabling users to import custom data and fine-tune small-to-medium foundation models with PEFT techniques using RL, reducing fine-tuning time from days to hours while maintaining <1% accuracy degradation.

Developed Distributed PyTorch high-throughput training and inference pipelines utilizing SageMaker's distributed libraries to efficiently train large-scale PyTorch models across multiple GPUs, significantly cutting down model iteration time.

Developed and optimized PyTorch models for NLP and Computer Vision tasks, managing the full model lifecycle from data preparation to deployment.

Automated and Streamlined Model Delivery: Automated CI/CD pipelines (leveraging Jenkins) for rapid, reliable deployment of new PyTorch model versions and inference configurations, resulting in a 75% reduction in deployment times.

Implemented text preprocessing pipelines for NLP tasks, deploying named entity recognition models to automate information extraction from documents.

Built Dockerized pipelines for scalable NLP model deployment, reducing development time by 40% and enhancing reproducibility.

Designed monitoring systems with CloudWatch, creating alerts and dashboards to track performance and reduce downtime by 30%.

Developed a Kafka-based stream processing engine to categorize and tag user complaints, improving query resolution time by 25%.

Designed big data processing systems using Spark and Hive to ingest and validate over 100,000 records daily, supporting data-driven decision-making.

Orchestrated shipment log collection and indexing: Enhanced productivity and maintained service level agreements by meticulously managing log data, contributing to a 15% increase in logistics efficiency