Python, Logging, Together, Model observability, Multi-model architectures, LLM integration, Model routing, Model cascades, Anthropic, Groq, Ensembles, LLM APIs, Inference platforms, Automated evaluation pipelines, Fallback mechanisms, Production Monitoring, Error handling, OpenAI, Prompt design, Model evaluation, Monitoring

Python, Containerized environments, Machine Learning Engineering, Applied AI, Data pipelines, Distributed training, Model evaluation

Pytorch, Machine Learning Algorithms, inference optimizations, MLOps practices, quantization techniques, SGLang, vLLM

Java, Nosql, Machine Learning, Perl, Natural Language Processing, Big Data, Python, Document Retrieval, Map-Reduce, Search Engine Technologies

Tensorflow, Pytorch, Gcp, MLops, Azure, Python, AWS, data preprocessing, model evaluation, feature engineering

production deployment , probability , Large Language Models (LLMs), Tensorflow, Nlp, Pytorch, Docker, Python, Predictive Modeling, Git, Kubernetes, scikit-learn, statistical learning, Portfolio Optimization, Model Evaluation, Mathematics, calculus, Optimization, LLM Evaluation, linear algebra, Generative AI, Signal Generation, Statistics, reinforcement learning, Time-series Forecasting, LLM Inference Optimization, Mathematical Modeling, Fine-tuning, quantitative modeling, Prompt Engineering

bedrock , Pytorch, Python, orchestration frameworks, LangChain, prompt tuning, vector search, fine-tuning, LLMs, RAG systems, high-availability inference systems, semantic embeddings, SGLang, vLLM, cloud environments, Evaluation, agentic workflows, AWS SageMaker, ML tooling ecosystem, scalable ML architecture, LlamaIndex

Machine Learning, Tensorflow, Pytorch, Python, Supervised machine learning, Production software engineering principles, Model optimization techniques, Hugging Face, Applied AI, Transformer-based models, Containerized environments, Large language models, Data pipelines, Distributed training

data preparation , text classification , tokenization , Machine Learning, Sentiment Analysis, Nlp, Summarization, MLops, LLM-powered Applications, Feature Engineering, Feature Extraction, MLflow, Entity Recognition, Retrieval-Augmented Generation, Embedding Generation, Model Deployment, Model Versioning, Large Language Models, Text Preprocessing, information extraction, AI Orchestration Frameworks, Generative AI, Transformer-based Models, Conversational AI, Prompt Engineering

Pytorch, Deep Learning, Python, Speech-to-text pipelines, Voice Activity Detection, Signal Processing, Algorithm and model development, Transcription, Machine Learning fundamentals, Diarization