Fine-tuning and retrieval-augmented generation (RAG) workflows, Python, Security compliance and ethical use of LLMs, LLM pipelines, CI/CD pipelines, Machine learning frameworks, Model validation testing, Machine Learning Operations, Prompt management and versioning systems, Observability tools for LLM applications, Multi Cloud skills

graph databases , C#, Machine Learning, Neo4j, Python, Java, GenAI, Hugging Face, GLM, ASTs, Arango DB, LangGraph, Qwen, Agentic frameworks, RAG architectures, LangChain, LLMs, Ai, LSP, vLLM, AutoGen, Ollama, DeepSeek, Llama, Tree-sitter, OpenAI

Tensorflow, Numpy, Git, Pandas, Pytorch, Gcp, Docker, FastAPI, Azure, Python, AWS, LangChain, scikit-learn, Qdrant, vector databases, Pinecone, async, LangGraph, Chroma, FAISS, RAG, Milvus, Weaviate

Machine Learning Algorithms, Python Programming, Prompt engineering, Data pipelines, Feature engineering

Apache Spark, Sql, Tensorflow, Pytorch, Databricks, Python, Machine Learning Algorithms, model versioning, deep learning frameworks, Lakehouse architectures, cloud platforms, MLflow, Monitoring, MLOps practices, Statistical Modeling, drift detection, CI CD for ML, automated retraining

Pytorch, MLops, Python, Kubernetes, Hugging Face, vLLM Triton, Vector Databases, LLM Gateway, Agentic AI, Self-hosted Models, RAG, LLM Generative AI, LLM Orchestration

Orchestration, C#, C++, Deep Learning, Numpy, Pytorch, Pandas, Decision Trees, Clustering, Python, Etl, LangChain, embeddings, vector search, agentic AI, tool calling, retrieval-augmented generation, CNNs, prompt engineering, LangGraph, ensembles