Cloud platforms (Azure preferred), Large Language Models (LLMs), Machine Learning Algorithms, Tensorflow, Pytorch, Python, Apache Spark, Sql, Databricks, Prompt engineering, GenAI application development, MLflow, Monitoring and drift detection, Agentic AI workflows, MLOps practices, Distributed computing frameworks, Deep learning frameworks, CI/CD for ML, Vector databases, Lakehouse architectures, Model versioning, Multi-LLM orchestration systems, Statistical Modeling, Embedding models, Automated retraining, Semantic search architectures

analog circuits , Pytorch, Python, EDA Tools, Scientific Computing, Spice, reinforcement learning

Apache Spark, ELT, Tensorflow, Pytorch, Gcp, Azure, Python, AWS, Etl, LangChain, Hugging Face, Pinecone, FAISS, ChromaDB, LlamaIndex

Machine Learning, Tensorflow, Jenkins, Docker, FastAPI, Python, Kubernetes, Pytorch, Uvicorn, Agentic AI, Encoder-decoder architectures, Pre-processing, Entity extraction, Fine tuning, Similarity matching, Text cleaning, Azure platform

Databases, Apis, Microservices, Python, AI application architecture, LLMs, cloud platforms, Agentic AI solutions, AI integrations, AI frameworks, Generative AI applications, AI ML frameworks

Machine Learning, Deep Learning, Microservices, Python, CrewAI, Generative AI, Agentic AI frameworks, Prompt engineering, LLMs, LangGraph, AutoGen, Vector Databases, Model evaluation techniques, RAG architectures

Amazon Web Services (AWS), Large Language Models (LLMs), Machine learning frameworks and libraries, Developing and deploying AI models in production environments, Python Programming Language, Cloud computing concepts and services