Python & API Development:
Design, develop, and maintain scalable web services using FastAPI or Flask.
Write efficient, reusable, and modular Python code to support API-driven LLM applications.
LLM & LangChain Implementation:
Build and optimize LLM-based applications using LangChain and related frameworks.
Develop custom pipelines for document indexing, retrieval, and summarization.
Integrate RAG capabilities with vector stores and retrievers for real-time querying.
Retrieval-Augmented Generation (RAG) Pipelines:
Architect and deploy RAG systems for chatbots, knowledge systems, and generative AI applications.
Optimize RAG models for speed, accuracy, and scalability.
Vector Stores & Retrievers:
Work with vector databases (Pinecone, FAISS, Chroma, Milvus) to store and manage embeddings.
Implement retrievers and re-rankers to improve query efficiency and response relevance.
AWS Cloud Deployment & Optimization:
Deploy and manage LLM-based applications on AWS (Lambda, EC2, S3, EKS, RDS).
Optimize model inference performance using quantization, distillation, and fine-tuning techniques.
Monitoring & Experimentation:
Implement real-time monitoring dashboards using Grafana, Prometheus, or Datadog.
Research and integrate cutting-edge generative AI advancements into production environments.