generative ai, explainable ai, AI-Generative AI, AI-Responsible AI, Conversational AI Platform, AI-AI Engineering, AI/ML Solution Architecture and Design, Technology, Responsible AI, Prompt Engineering

Databases, API design, Distributed Systems, Python, LangChain, CrewAI, LLMs, AutoGen, backend architecture, Evaluation, guardrails, LLM orchestration, LlamaIndex

Databases, Machine Learning, Apis, Sql, Tensorflow, Deep Learning, Numpy, Nlp, Pandas, Pytorch, MLops, Python, model optimisation, ML algorithms, Scikit-learn, deployment concepts, large-scale data processing, Statistics

Keras, Sql, Pytorch, Java, Python, Tensorflow, R, scikit-learn

Gitlab, Sql, Tensorflow, Pandas, Pytorch, Gcp, Git, XGBoost, Github, Numpy, AWS, Kubernetes, Python, Azure, Docker, Jenkins, Airflow, GitLab CI, scikit-learn, CoreML, Neptune.ai, TensorRT, GitHub Actions, MLflow, LightGBM, TensorBoard, TFLite

RAG, Python programming language, AWS AI ML services ecosystem, FAST APIs, Machine Learning architectures

text classification , MLops, Python, AWS, multi-agent systems, cloud-based AI deployments, Llm, RAG, AI frameworks, retrieval systems, embeddings, Evaluation, model fine-tuning, Optimization, semantic search, NLP systems, ML and NLP models, reinforcement learning

Machine Learning, Natural Language Processing, Kafka, Tensorflow, Django, Gcp, Pytorch, Docker, Flask, Azure, Kubernetes, Python, AWS, GenAI, Scikit-learn

data engineering , regression models , Machine Learning, MLops, Sql, Python, Deep Learning, anomaly detection, Classification Models, Boosted Models, Simulation Problems