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

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

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, 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

Nlp, Deep Learning, Memory, vector search, embeddings, ML AI pipelines, RAG systems, GenAI models, customer-facing applications, CI CD, Agentic AI solutions, enterprise APIs, multi-agent workflows, chunking, retrieval, time series forecasting, model monitoring, LLM inference architectures, Demand Forecasting, data platforms, MLOps pipelines, prompt engineering, planning, tool-calling, Inventory Planning, drift detection, Governance, autonomous agents