Tensorflow, Machine Learning, Pytorch, Azure, Google Cloud, AWS, Deep Learning, Feature Engineering, Data Preprocessing

Pytorch, NLP techniques and libraries, Python with TensorFlow, Basic MLOps practices, Azure ML or equivalent cloud ML services

data engineering , .NET, production deployment , Java, Scalability, Data Science, Cloud, System Design, Backend Engineering, Integration, Python, ML Engineering, Generative AI, LLMs, Agentic AI, Security, RAG, architecture, AI frameworks, AI application development

Modern software engineering practices, semantic search, Generative AI, Embeddings, AI model evaluation, AI application architecture, AI Agents, Prompt optimization, Vector databases, AI safety best practices, Prompt Engineering

Django, Git, Docker, FastAPI, Rest Apis, Python, Microservices, PostgreSQL, Prometheus, Kafka, Grafana, Datadog, Sql, Nosql, Gcp, MySQL, MongoDB, Splunk, Azure, Kubernetes, AWS, Generative AI, LLMs, CI CD automation, AI ML concepts, RAG, Event-driven architectures

data preparation , prophet , Sql, Tensorflow, Numpy, Pandas, MLops, Pytorch, Gcp, Azure, Python, AWS, Generative AI, Feature Engineering, Scikit-learn, Model Optimization, CI CD, Agentic AI, Time Series Forecasting, Hyperparameter Tuning, RAG, Large Language Models

MLops, Python, RAG deployment patterns