Neural Networks, Docker, Computer Vision, RAG expertise, RAG architecture, LLM Fundamentals, LLM VLM fine-tuning, OCR tools, Prompting techniques, Custom LLM agent design, AI agents tool use, Image processing concepts, Transformer architecture, Object detection concepts, Git operations, API scaling, Frontend UI basics, MCP Model Context Protocol, Vision agents, Flask API development, Audio handling in agent workflows, Database selection

Kibana, Sklearn, Kafka, Redshift, Redis, Tensorflow, Rabbitmq, Kinesis, Docker, Spark, Flask, MongoDB, Pytorch, Kubernetes, Airflow, Mlflow, Sagemaker

Tensorflow, Machine Learning, Gcp, Pytorch, Azure, Python, Sql, AWS, scikit-learn

Apis, Python, LangChain, embeddings, LLMs, vector databases, knowledge graph concepts, production-grade applications, Anthropic models, RAG, AI agents, OpenAI, LlamaIndex

Tensorflow, Pytorch, Docker, Apache Spark, Kubernetes, Python, AWS SageMaker, Scikit-learn, MLflow

Keras, Natural Language Processing, Pytorch, Deep Learning, Tensorflow, Python, Pandas, Numpy, Machine Learning, machine translation

Github, Sql, Computer Vision, Deep Learning, Opencv, Machine Learning, BigQuery, Pytorch, Kubernetes, Python, Docker, Gcp, Scikit-learn, Jupyter, dbt

Tensorflow, Numpy, Pytorch, Python, AWS, Scipy, Sql, React, Pandas, Gcp, Keras, Rest Apis, Azure, RNNs, Deep Learning architectures, CNNs, LLM APIs, Chain-of-Thought, distributed data processing frameworks, open-weight open-source models, classical ML algorithms, hybrid search techniques, Few-shot prompting, Vector Stores, Transformers, Advanced Prompt Engineering techniques