Git, Testing, LangChain, Knowledge retrieval systems, Prompt engineering, RAG systems, Vector databases, Development frameworks and tools, Advanced Python programming, LLM optimization techniques, Embedding models, RAG pipelines

error analysis , tokenization , Machine Learning, Nlp, Pytorch, Python, Generative AI, Feature Engineering, model validation, Vector Search, Retrieval-Augmented Generation, Embeddings, Model Performance Tuning, Data Processing

Tensorflow, Pytorch, Terraform, Docker, XGBoost, Python, BigQuery, Bash, Dataproc, Sql, MLops, Spark, DataFlow, Kubernetes, GenAI, Gemini Agent Builder, Cloud Build, Vector Search, GitHub Actions, Vertex AI, Kubeflow, RAG architectures, GKE, Scikit-learn, Vertex Pipelines, Model Deployment Automation, CI/CD

Microservices, Distributed Systems, HITL, Python, LangChain, CrewAI, Prompt engineering, time-series modeling, gRPC APIs, LangSmith, AutoGen, LangGraph, Embeddings, scalable AI infrastructure, RAG, AI agents, LLM orchestration, Tool calling, LLM evaluation, cloud-native development

tokenization , Unsupervised Learning, Deep Learning, Tensorflow, Machine Learning, Big Data Technologies, Nltk, Pytorch, Information Retrieval, Python, Text Mining, Nlp, Distributed Computing, GenAI, Lemmatization, Feature Engineering, Parallel Processing, Stemming, R, SpaCy, semantic analysis

Orchestration, Django, React, FastAPI, Python, multi-step reasoning, Agentic AI, tool function calling, LLM APIs, Vector databases, RAG pipelines

Sql, Tensorflow, Numpy, Pandas, Pytorch, XGBoost, Python, Vector search, LangChain, Prompt engineering, RandomForest, LightGBM, LangSmith, Scikit-Learn, AutoGen, LangGraph, Agentic AI workflows, Embedding models, RAG architectures

containerization , Machine Learning (ML), Aws Services, Generative AI, Security best practices, Infrastructure-as-code tools, CI/CD pipelines, Orchestration technologies, ML/AI infrastructure