data engineering , Numpy, Pandas, Pytorch, Python, scikit-learn, ML pipelines

Git, Docker, Machine Learning Algorithms, Sql, Python, Feature engineering, Model evaluation

Tensorflow, Pytorch, Docker, Python, AWS, Sql, Gcp, MLops, Spark, Azure, Kubernetes, Anthropic Claude, Multi-agent frameworks, Azure OpenAI, Agentic AI systems, Mistral, RAG architectures, LLMs, Prompt engineering strategies, Scikit-learn, model deployment, Fine-tuning pipelines, Agentic AI, Llama, RAG, OpenAI, Gemini

Natural Language Processing, Deep Learning, Python, Scala, Machine Learning, Search Technologies, Retrieval Augmented Generation, Large Language Models, reinforcement learning, Go, Embeddings

Python, Tensorflow, Pytorch, Docker, Kubernetes, Cloud Platforms

, Sql, Sqlite, Pandas, Pytorch, Information Retrieval, XGBoost, Matplotlib, S3, Postgres, AWS, Python, Docker, Jenkins, LabelBox, OpenSearch, MosaicML, ML pipelines, DVC, NVIDIA NeMo, Amazon EKS, Scikit-learn, Jupyter, LightGBM, HuggingFace, Transformers

Machine Learning, data mining, Deep Learning, Tensorflow, MLops, Gcp, Pytorch, Databricks, Azure, Python, AWS, scikit-learn, data processing transformation tools, cloud environments, Optimization, big data infrastructures, CI CD for ML monitoring

Tensorflow, Machine Learning, MLops, Pytorch, Natural Language Processing, Python, Sql, Deep Learning, Vector Databases, Generative AI, Retrieval-Augmented Generation

containerization , Github, Unit Testing, Tensorflow, Numpy, Docker, Python, Scipy, Pandas, Kubernetes, Workflows, Databricks, CI CD Pipelines, scikit-learn, Cluster Creation, Edge Deployment, Azure Cloud Architecture for ML, ACR, Repo Management, Portainer, ML Model Design, Deployment