Neural network design principles, Deep learning frameworks, Cloud platforms, Machine Learning Operations, AI model lifecycle management, Containerization and orchestration tools, Generative AI models

Tensorflow, Automation Tools, Pytorch, Python, GenAI tools, data preprocessing, scikit-learn, LLM integration, API-based deployment, prompt engineering, GenAI integration, workflow automation, ML pipelines, ML model development, feature engineering, model experimentation

Prometheus, Grafana, Tensorflow, Cloudwatch, Pytorch, Docker, AWS ECS, AWS CodePipeline, Rest Apis, Python, Scikit-learn, vector databases, Pinecone, GRPC, async job processing, GitHub Actions, OpenTelemetry, FAISS, CI CD pipelines

Apis, Docker, FastAPI, Python, Kubernetes, Multi-Agent Communication, enterprise engineering tools, cloud platforms, MCP Servers, Enterprise Connectors, Workflow Engine, VS Code integrations, Agent SDK, Shared Prompt Library

spatial data , Databricks, Python, Computer Vision, XGBoost, MLops, ONNX, data pipelines, classical ML fundamentals, model evaluation, quantization, time-series methods

Python, Sql, Production ML, Embeddings

BigQuery, Gcp, FastAPI, LangChain, Async Python patterns, pgvector, Claude, Pinecone, LangSmith, LangGraph, OpenAI APIs, Vertex AI, AI LLM Engineering, vector DBs, Ragas, Gemini, Weaviate