Machine Learning, Sql, Python, GenAI, API tool interfaces, structured outputs, prompting, experimentation, Statistics, foundation models, RAG, feature engineering, function tool calling, agent orchestration

Pyspark, AWS Glue, Tableau, Tensorflow, Pytorch, Docker, Terraform, Flask, Python, Aws Lambda, Power Bi, Sql, FastAPI, Kubernetes, AWS Step Functions, AWS Lake House, MLflow, CI CD, PyCaret, GitHub Actions, Streamlit, Scikit-learn, AWS SageMaker, Amazon Bedrock

containerization , Unit Testing, Tensorflow, Numpy, Nlp, Pytorch, Python, Machine Learning, Data Transformation, Api Development, Sql, Data Quality, Git, Pandas, Computer Vision, MLops, Spark, Clustering, Airflow, data pipelines, scikit-learn, MLflow, Statistical Modeling, graph analytics, LLMs, code reviews, tree-based algorithms, CI/CD, feature engineering

Unix Shell, Python, Sql, machine learning techniques

Automated Testing, Security Testing, Python, Api Integration, Sql, Git, GenAI, agentic AI, tool calling, production troubleshooting, knowledge bases, Dify, agent state, audit trails, LLMs, Tencent CodeBuddy, retrieval-augmented generation, conversational flows, permissions input and output validation, evaluation pipelines, Google ADK, Amazon Bedrock, approval workflows, agent development platform

statistical concepts , Machine Learning, Python, R, Data Science Tools and Technologies, statistical programming

Programming Languages: Python, SQL Machine Learning: Regression, Model Evaluation Metrics Machine Learning Libraries: pandas, Scikit-learn Deep Learning Frameworks: TensorFlow, PyTorch Generative AI / LLM: GenAI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LLM Evaluation Statistics & Analytics: Statistical Analysis, Advanced Analytics Data Visualization & Reporting: Data Visualization, Business Intelligence (BI) Tools MLOps / LLMOps: Model Versioning, LLMOps DevOps & Deployment: CI/CD, Kubernetes API & Cloud Technologies: APIs, Cloud ML Services End-to-End Data Science: Problem Framing, Exploratory Data Analysis (EDA), model development , Recommendations Presentation Telecom / ISP Analytics: Churn Analysis, Average Revenue Per User (ARPU), Net Promoter Score (NPS), Clustering, Hypothesis Testing, Numpy, MLops, Docker, Classification, Time-Series Analysis, Prompt Engineering, Statistical Modeling, Data Exploration, Data Storytelling, Notebooks, Model Registry, Model Monitoring, Retraining Triggers, Cloud AI Services, Model Evaluation, Usage Analytics, Campaign Metrics, Subscription Business Metrics

MLops, Machine Learning, Statistical Analysis, risk management, credit modelling, AI-assisted tools

Azure ML, SAS, Scala, Python, R, AWS Sagemaker, Google AI