.NET, Microservices, Python, AWS, Apis, Sql, Devops, Gcp, Azure, AI Agents, pgvector, MCP Server, AI Governance, Pinecone, Security, OpenSearch, Vector Databases, CrewAI, LangChain, LLMs, Semantic Kernel, AutoGen, Enterprise AI Architecture, FAISS, RAG, LlamaIndex

Microservices, Tensorflow, Pytorch, Docker, Azure, Kubernetes, Python, AWS, LangChain, enterprise search solutions, vector databases, LangGraph

Machine Learning, Apis, Neural Networks, Dynamo Db, Python Programming, Solution Architecture, Prompt tuning, AWS Bedrock, Prompt engineering, Similarity search using vector databases, Embedding generation, Presales GTM, Llm, Agentic AI, Claude LLMs, Document ingestion

Google Cloud Platform, Tensorflow, Pytorch, Docker, Terraform, Openshift, Kubernetes, Python, GKE, Feature Store, Anthos, Model Registry, Vertex AI Pipelines, Vertex AI, Prediction services

API design, Distributed Systems, AI Platform architecture, Exposure to cloud ML tooling and MLOps practices, Reliability engineering, Building platform capabilities for AI ML or GenAI solutions

data engineering , containerization , Machine Learning, Cloud Architecture, Big Data, Api Development, Microservices, DevOps Practices, Model Deployment, Security Protocols

Data Modeling, ELT, MLops, Etl, LLM ecosystems, Generative AI, Enterprise data platforms, Data engineering fundamentals, AI ML lifecycle, Cloud ecosystems, Enterprise architecture patterns, API integrations, AIOps

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

containerization , AWS, Kubernetes, Python, Azure, Orchestration, Docker, Large Language Models, AI ML frameworks, observability, DevOps practices, CI CD pipelines

Ml, Deep Learning, React, Nlp, Agentic Frameworks, cloud-based AI deployments, LLMs, One-shot, Few-shot, Azure AI, LangChain, AWS Bedrock, Prompt engineering