Tensorflow, Numpy, Pytorch, Docker, XGBoost, Python, Pandas, MLops, Azure Machine Learning, scikit-learn, vector databases, Azure AI Foundry, LangGraph, Azure AI Search, Application Insights, LangChain, experiment tracking, RAG architecture, LLMOps, Azure OpenAI Service, LightGBM, embedding models, Semantic Kernel, CI/CD pipelines, Azure AI Content Safety, Azure App Service Functions

software engineering practices , Machine Learning, Data Structures, Deep Learning, Tensorflow, Git, Algorithms, MLops, Pytorch, Docker, Kubernetes, Python, Generative AI, Scikit-learn, Cloud Platforms, Model Deployment

software engineering practices , Apis, MLops, Microservices, Python, DataOps, LLMOps, architecture patterns, AI architectures, prompt engineering, cloud-native development

Summarization, Python, RAG components, LLM pipelines, citation grounding

snowflake , Spark SQL, Pyspark, Databricks, Azure, Python, embeddings, LLM API integration, API integration patterns, vector databases, cloud-native delivery, CI CD, prompt workflows, Delta Lake, agent orchestration

Numpy, Pandas, Gcp, FastAPI, Azure, Python, AWS, GenAI, embeddings, LLMs, prompt engineering, RAG

Sdlc, System Design, Databricks, FastAPI, Python, AWS, LangChain, Generative AI, Enterprise AI platforms, Vector search systems, LLMs, AI agent development, Azure AI Foundry, Semantic Kernel, LangGraph, Agentic AI, Embedding pipelines, Production grade scalable AI systems, Transformers, Cloud native architectures, Multi-agent orchestration

production readiness , Python, ML training and evaluation workflows, scalable system design, data pipelines, GenAI LLM application development, deployment observability, model pipelines, APIs and backend services, ML LLM framework

Nosql, Java, Restful Apis, Kubernetes, Python, Sql, AI productivity tools, Go, AI ML technologies