C, shell scripting, Python, AI model inference, accuracy-performance tradeoffs, debugging performance issues, AI deployment stacks, quantization

Big Data Technologies, Python, containerization technologies, cloud platforms, ML libraries, GenAI tooling, monitoring and logging tools, R, MLOps practices and tools, machine learning algorithms and techniques, data processing tools, model monitoring, LLM inference lifecycle

Pyspark, Debugging, Databricks, Python, Sql, Azure DevOps, Data Analysis

Machine Learning, Tensorflow, Pytorch, Transformer, audio speech synthesis, U-Nets, audio and speech generative networks, speech restoration, feature extraction techniques, Statistical Modeling, BERT, DL algorithms, BART, diffusion models

Pytorch, Python, SageMaker Studio, model evaluation, deep learning frameworks, transformers for NLP tasks, Experimental Design

MLops, Databricks, Python, Pandas, XGBoost, Spark, LIME, LightGBM, MLflow, scikit-learn, SHAP

Arm, Sql, Azure Machine Learning, MLops, Python, Terraform, API-based integrations, Virtual Agent, ServiceNow AI ML Predictive Intelligence, Bicep, Azure OpenAI, Infrastructure as Code, Kore.ai XO Platform, Microsoft Azure AI, CI CD pipelines

Machine Learning, Hadoop, Natural Language Processing, Information Retrieval, data mining, Netezza, Redshift, Spark, Generative AI, Forecasting, anomaly detection, Teradata, Classification, Software Programming, Statistics, Large Language Models

Power Bi, Tableau, Microsoft Azure, Rest Apis, AWS, Version control Git, JSON service integration patterns, CI CD fundamentals, Applied understanding of ML lifecycle concepts, Object-oriented design, Python backend services, ELT concepts, Agent orchestration frameworks, SQL advanced querying, Data pipelines

Django, FastAPI, Microservices, Python, Flask, Distributed Systems, Machine Learning