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

workflow engines , Python, tool function calling, tracing observability tooling, Logfire, AI systems in production, prompting, Temporal, OpenTelemetry, pydantic-ai, LiteLLM, MCP, agent architectures

Machine Learning, Deep Learning, Tensorflow, Numpy, Git, Pytorch, Pandas, Opencv, Image Processing, Keras, Predictive Analytics, Python, Computer Vision, Transfer Learning, Scikit-learn, Convolutional Neural Networks, Quantization, Model Optimization, Jupyter Notebook, Edge AI Deployment, anomaly detection, Time-series Analysis

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

Python, vector stores, data pipeline development, retrieval strategies, re-ranking, multi-step agent workflows, embedding models, prompt engineering, RAG architectures