WMS), Enterprise platforms (ERP, MLops, Software Engineering, Reasoning engines, LangChain, CrewAI, Planning Modules, Tool API integration, LLMs, LLM-driven autonomy, LLMOps, AI agent ecosystems, Perception interfaces, AI/ML system development, Agentic AI systems, AutoGen, CRM, Scm, Multi-agent systems, Distributed AI systems, Memory systems

System Architecture, Python, Gen AI, ML Ops, LLM Ops, Fast API, Responsible AI, Database Integration

Java, Api Integration, Tensorflow, Pytorch, Gcp, Azure, Python, AWS, LangChain, Vector databases, Hugging Face, Pinecone, Llm, Gen AI, LangGraph, microservices architecture, FAISS, RAG, Weaviate

Machine Learning, Azure Databricks, Tensorflow, Azure Synapse, Azure Data Factory, MLops, Pytorch, Docker, Data Lake, Azure Machine Learning, Kubernetes, Python, Azure DevOps, Databricks dashboards, LLMOps, Cognitive Services, Scikit-learn, MLflow, Azure OpenAI, Azure AI Foundry, Azure AI, Microsoft Fabric

Azure Databricks, Sql, Tensorflow, Azure Data Factory, Pytorch, Docker, Azure Synapse Analytics, Azure Machine Learning, Kubernetes, Python, Azure DevOps, Azure OpenAI Service, Scikit-learn, GitHub Actions, Azure AI Search, Azure Cognitive Services

.NET, Azure, Python, OpenAI API, Langchain

Express.Js, Jwt, Node.js, Grafana, Mssql, Angular, React, Typescript, Azure Ad, MongoDB, Restful Apis, Azure, Jest, LangChain, GenAI, Playwright, GitHub Copilot, OpenTelemetry, rbac, Next.js, RAG

Mobile Application Development, APEX, Ml, Flows, LLM-based systems, agentic tool-use patterns, agentic systems architecture, automation platforms, Agentforce, platform scalability principles, Lightning Web Components, responsible AI, LLM orchestration, integration patterns, Salesforce platform architecture, cloud-native architecture, Ai, AI risk frameworks, prompt engineering, Salesforce Field Service solution development, Agentforce custom agent frameworks, AI agents, AI governance, Gemini

Terraform, Spark, Python, Kubernetes, Sql, Containers, Databricks Asset Bundles, Cloud storage integrations, Model deployment, Feature engineering workflows, MLflow model registry, Production ML operations, Workflow orchestration, Distributed processing workloads, Observability practices, Operational monitoring tools, Experiment tracking, Model-serving patterns, Databricks workspaces, Git-based CI/CD

Generative AI (GenAI), GCP), Cloud platforms (AWS, Azure, Databricks, LangChain, CrewAI, NIST AI RMF, AWS Bedrock, AI governance and compliance frameworks, Ai, Anthropic, LLMOps pipelines, Microsoft RAI Maturity Model, LangGraph, AI/ML specializations, EU AI Act, RAG, ISO 42001, Multi-agent architectures, Azure AI, OpenAI