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

react.js , PostgreSQL, Tensorflow, Pytorch, Docker, Terraform, Flask, Python, Express, Node.js, Vue.JS, Git, FastAPI, pgvector, Qdrant, Hono, Pinecone, Anthropic SDK, GitHub Actions, OpenAI SDK, Chroma, Next.js, LangChain, CrewAI, n8n, Tailwind CSS, GitLab CI, Weaviate, Flowise, LlamaIndex

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, AWS, Generative AI, Agentic AI, Azure AI, CI/CD

AI ML, Python, Llm, RAG, Gen AI, AI Engineer