error analysis , Tensorflow, Pytorch, Docker, Python, LangChain, Evaluation metrics, pgvector, Qdrant, Statistical testing, Container orchestration, MLflow, Pinecone, LLM orchestration frameworks, API integrations, Cloud deployments, Vector search engineering, LlamaIndex, MLOps fundamentals

Svm, XGBoost, Python, Decision Trees, Clustering, MLops, Restful Apis, Agent reasoning patterns, Prompt engineering, Feature selection, scikit-learn, RAG systems, Data preprocessing, Conversation analytics, AI system observability frameworks, LLM conversational AI systems, Feature engineering, semantic search, AWS Sagemaker, LangChain, CrewAI, Vector databases, LightGBM, Ensemble models, Regression, Statistical Modeling, Agent frameworks, Context optimization

Machine Learning, Nlp, Pytorch, Docker, Rest Apis, Kubernetes, Python, Hugging Face Transformers, RAG, vector databases

Machine Learning, Deep Learning, Tensorflow, Pytorch, Opencv, Python, Computer Vision, GenAI, Prompting, LLMs, GenAI systems, OCR engines, PIL, Fine-tuning

error analysis , Deduplication, Pytorch, Python, Cloud Storage, Statistical Analysis, data augmentation, model training, model-based labeling, distributed compute, Active Learning, VLMs, Hugging Face LLMs, ablation studies, dataset curation, benchmark evaluation, workflow orchestration, weak supervision, Batch Processing, inference evaluation, synthetic-data generation, Production Monitoring, data-quality measurement, diffusion models, multimodal models

Java, Docker, Kubernetes, Python, TFX, AWS SageMaker, Kubeflow, Google Vertex AI, Go, MLflow, Metaflow

data engineering , model selection , Numpy, Pandas, Pytorch, Python, scikit-learn, ELT data pipelines, ML research methodologies, Production ML systems, ML pipelines, Hypothesis formulation and testing, Cross-validation, Performance benchmarking, Reproducible research