• Architected a scalable GenAI enterprise knowledge platform leveraging LLMs, RAG pipelines, and LangChain on Oracle Cloud Infrastructure, reducing knowledge retrieval time by 60% for 8,000+ global users.

• Led development of a multi-agent AI orchestration system using LangGraph and LlamaIndex, enabling automated SQL generation and intelligent report synthesis, resulting in 4x improvement in analyst productivity.

• Fine-tuned LLaMA 3 models using LoRA and QLoRA on 50K+ domain-specific documents, achieving 91% answer relevance, and deployed via FastAPI and AWS Lambda with sub-120ms end-to-end latency.

• Optimized LLM inference pipelines using NVIDIA CUDA, PyTorch quantization, and Azure Kubernetes Service (AKS), reducing serving latency by 42% while supporting 250K+ daily requests at 99.9% uptime.

• Designed and implemented MLOps and production deployment standards using Docker, Kubernetes, MLflow, and CI/CD pipelines on GCP Vertex AI, reducing release cycle time by 50% across 15+ production-grade ML systems.

• Designed and deployed NLP document classification pipelines using BERT and Hugging Face Transformers, processing 1M+ legal and compliance documents and improving extraction accuracy by 85% across 6 enterprise clients.

• Built scalable end-to-end ML training and retraining pipelines using Apache Airflow and AWS SageMaker, reducing retraining cycle time by 48% and enabling automated weekly model refreshes across 7 production systems.

• Developed customer churn prediction models using XGBoost and LightGBM on large-scale CRM datasets, achieving 91% AUC and driving data-backed retention strategies that reduced churn by 25%.

• Engineered computer vision defect detection systems using ResNet-50 and PyTorch, achieving 93% classification accuracy and reducing manual inspection costs by 40% in manufacturing workflows.

• Led adoption of MLflow for experiment tracking, model registry, and lifecycle management across 10+ ML projects, standardizing MLOps workflows, improving model governance, and reducing production incidents by 38%