Nlp, Deep Learning, Memory, vector search, embeddings, ML AI pipelines, RAG systems, GenAI models, customer-facing applications, CI CD, Agentic AI solutions, enterprise APIs, multi-agent workflows, chunking, retrieval, time series forecasting, model monitoring, LLM inference architectures, Demand Forecasting, data platforms, MLOps pipelines, prompt engineering, planning, tool-calling, Inventory Planning, drift detection, Governance, autonomous agents

Amazon Web Services (AWS), Large Language Models (LLMs), Machine learning frameworks and libraries, Developing and deploying AI models in production environments, Python Programming Language, Cloud computing concepts and services

Java, BigQuery, Apis, Cloud Storage, Python, GenAI, Prompt engineering, Agent loops, Google Enterprise Agent Platform, Pub Sub, RAG concepts, Embeddings, Agentic AI, Memory fundamentals, GCP Services, SDKs, Tool integration

Machine Learning, Data Analytics, Deep Learning, MLops, Performance Modeling, Distributed Systems, Python, multi-agent frameworks, decision systems, AI system design, power performance optimization workflows, Agentic AI autonomous systems, Power Shell scripting, cloud platforms, system debug, reinforcement learning, platform technologies, Optimization Techniques, adaptive agents, production deployment of AI systems, scalable pipelines

Hive, Pytorch, Spark, Python Programming, Keras, Sql, Tensorflow, AI ML solutions, Scikit-learn, machine learning techniques

Hypothesis Testing, Sql, Tensorflow, Pytorch, Decision Trees, Arima, Keras, Python, Statistical Analysis, KubeFlow, exponential smoothing, support vector machines, Great Expectations, Z-Test, probabilistic graph models, T-Test, Regression, MXNet, Evidently AI, CNTK, ARIMAX, Sci-Kit Learn, BentoML

Sql, Python, tree-based models, R, observability monitoring tools, Generative AI tooling, time-series forecasting, classical ML modeling

Tensorflow, Git, Pytorch, Gcp, Docker, Keras, Azure, Kubernetes, Python, AWS, LangChain, Gen AI, Agentic AI, Prompt Engineering