Apis, System Design, Microservices, LLM capabilities and limitations, Backend Architecture, Enterprise-scale AI Platforms, AI ML Data Engineering, distributed architectures, Scalable backend systems

Tensorflow, Pytorch, Python, LLM Fine-Tuning, Edge On-device AI Deployment, TensorRT, RAG Pipelines, Pinecone, llama.cpp, Optimization, ONNX, Vector Databases, GGUF, semantic search, FAISS, Model Quantization, Transformer Architectures, ChromaDB, Generative AI LLMs, Embedding Models, Prompt Engineering

sdk development , Microsoft Azure, Python, LLM observability, AI coding assistants, Batch async API patterns, AI Gateway operations, LLM API design, API lifecycle management, LangGraph ADK, Agent frameworks, OpenTelemetry, Guardrails, Telemetry observability, GCP Vertex AI

sdk development , Python, API lifecycle management, AI Gateway operations, LLM APIs, OpenTelemetry, LangGraph ADK, Telemetry observability

snowflake , Sql, Git, Gcp, Docker, FastAPI, Azure, Python, AWS, LangChain, CrewAI, LLMs, Semantic Kernel, LangGraph, Dataiku, LlamaIndex

Tensorflow, Pytorch, Python, Optimization Techniques, ONNX, ChromaDB, Embedding models, Vector databases, INT8, Transformer architectures, model quantization, LLM fine-tuning, GGUF, 4-bit, semantic search, Pinecone, RAG architectures, FAISS, prompt engineering, llama.cpp, TensorRT

Machine Learning, Python Programming, Frameworks, LLMs, RAG, scalable AI

C, Python, Linux OS fundamentals