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

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

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

Api Management, Uipath, Aws Services, Power Automate, Functions, Rpa Tools, Lambda, Docker, Api Gateway, AWS Bedrock, AKS, Azure OpenAI, AI automation architectures, Azure services, Microsoft Power Platform, CI CD pipelines, container orchestration

Sql, Tensorflow, Numpy, Git, Pandas, Pytorch, Databricks, Rest Apis, Python, Scikit-learn, ADLS Gen2, MLflow, Delta Live Tables, Azure Data Lake Storage, Delta Lake, AI ML libraries

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