Sql, Microservices, Angular, Tensorflow, React, Git, Gcp, Docker, Data Visualization, Restful Apis, Azure, Kubernetes, Python, AWS, Machine Learning Algorithms, data pipelines, NoSQL databases, Scikit-Learn, data lakes, R

Apis, MLops, Docker, Kubernetes, Python, AWS, Generative AI, Amazon EC2 P5, AI agent architectures, NVIDIA GPUs, Amazon SageMaker, Amazon Bedrock

Familiarity with GCP and or other major cloud AI platforms, Prompting RAG fine-tuning and model comparison concepts, Python and basic experimentation workflow, Cost-performance analysis and technology assessment, Documentation and structured research reporting, Data analysis and evaluation methods, LLMs model evaluation and benchmarking, Understanding of open-source and closed-source model ecosystems, AI infrastructure literacy including cloud deployment concepts

Docker, Cloud deployment, FastAPI, Python, LangChain, LangGraph, multi-agent systems, security fundamentals, vector databases, AI LLM systems

Python, Javascript, prompt engineering, LLMs

Networking, Json, Identity And Access Management, Microservices, Cloud Infrastructure, Rest Apis, Python, CI CD methodologies, data retrieval techniques, workflow orchestration, secure cloud architectures, low-code AI integration, agent-based AI, prompt engineering, Responsible AI principles, AI and LLM engineering, Microsoft Power Platform, RAG solutions, Azure AI services, observability