Deploying machine learning models in production, Data preprocessing, Machine Learning Operations, Cloud AI services, Feature engineering

Machine Learning, Tensorflow, Pytorch, Gcp, Azure, Python, AWS, Generative AI, Prompt engineering, scikit-learn, LLM integration, RAG pipelines, Fine-tuning

Tensorflow, Numpy, Git, Pandas, Pytorch, Docker, Azure, Kubernetes, Python, scikit-learn, Segmentation, AKS, CNNs

Machine Learning, Apache Spark, Pandas, Gcp, Pytorch, Azure, AWS, LangChain, scikit-learn, HuggingFace, data science tools, Text2SQL fine-tuning, GenAI applications, DSPy, RAG multi-agent systems, Databricks Intelligence Platform

Cloudformation, Jenkins, Git, MLops, Gcp, Terraform, Spark, FastAPI, Azure, Kubernetes, Python, AWS, RAG systems, MLflow, vector databases, TF Serving, statistical hypothesis testing, GitHub Actions, TorchServe, Go or Rust

CDK, Nlp, Terraform, Python, Devops, vector search, embeddings, AWS Bedrock, HuggingFace, Azure AI Foundry, LangGraph, AWS Sagemaker, ML observability, LLM architectures, RAG pipelines, explainability tools, LangChain, real-time NLP, PEFT, Generative AI, Azure OpenAI Service, Azure ML Studio, Ai, fine-tuning methods, infrastructure-as-code, LoRA, Transformers, SmolAgents, LlamaIndex

Machine Learning, Python, Sql, Statistical Analysis, Generative AI, R, observability monitoring tools, full-stack development

LLMs (OpenAI, Data Processing & Feature Engineering, Cloud Platforms (AWS/Azure/GCP – optional), Python, Pytorch, LangChain, NLP Concepts