Machine Learning, Git, Gcp, Pytorch, Docker, Opencv, Azure, Python, Computer Vision, AWS, Linux Based OS, R?CNN

prophet , Power Bi, Tableau, Sql, Deep Learning, Azure ML, Arima, Python, Machine Learning Algorithms, LSTM, ML libraries, MLOps platforms, AI systems, Classification, database structures, data visualization tools, DataRobot, ensemble methods, Abacus.ai, ML pipelines, Regression, time series forecasting

Micro Services, Numpy, React, Pandas, Rest Apis, Python, Hugging Face, Claude, prompt engineering, LLM APIs, Next.js, RAG pipelines, OpenAI

Pytorch, Python, PEFT, GPU usage, LLMs, diffusion pipelines, inference latency, vector databases, LoRA, VRAM efficiency, RAG, quantization, Stable Diffusion

Jax, Tensorflow, Pytorch, Docker, Kubernetes, Python, LangChain, CrewAI, Qdrant, Pinecone, AutoGen, GCP Vertex AI, AWS SageMaker, Milvus, Azure AI, Weaviate, LlamaIndex

API design, React, Docker, Neo4j, Distributed Systems, Flask, Python, Redis, Cloud Infrastructure, FastAPI, tool calling, Hugging Face Transformers, Qdrant, Gradio, Pinecone, AWS Neptune, LLM APIs, vector search strategies, Streamlit, PEFT, DeepSpeed, model fine-tuning, RAG architecture, Accelerate, agent-based systems, prompt engineering, observability for AI systems, FAISS, LoRA, async workflows

Sql, Microservices, Tensorflow, Git, Gcp, Pytorch, Azure, Python, AWS, MLflow, vector database management, test-driven development, model quantization techniques, Go or Rust

Pyspark, Azure Databricks, ELT, Tensorflow, Gcp, Pytorch, Docker, Azure, Kubernetes, Python, AWS, Etl, data pipelines, scikit-learn, Delta Lake, data lakehouse architecture, Machine Learning algorithms and techniques

Tensorflow, Sql, Prometheus, Splunk, Pytorch, Python, Elk, Pandas, Spark, MLflow, Flink, Scikit-learn