Neural Networks, Deep learning techniques, Scalable AI systems, Cloud platforms, Machine Learning Operations, Troubleshooting and optimizing AI model performance, Generative artificial intelligence models, Deployment pipelines for AI applications

Apis, Performance Monitoring, Version Control, Gcp, MLops, Azure, Python, Statistical Analysis, Machine Learning Algorithms, AWS, data preprocessing, cloud platforms, production-ready ML pipelines, model evaluation, deep learning techniques, feature engineering, model lifecycle management, model optimization, hyperparameter tuning

triton , Pytorch, Kubernetes, Python, Docker, Qdrant, vLLM, LlamaIndex, Airflow, Weaviate, pgvector, LangChain, TGI, CrewAI, Pinecone, LangGraph, GPTQ, MLOps tools, Kubeflow, MLflow, quantization techniques, Scikit-learn, AWQ, Hugging Face ecosystem, AutoGen, GGUF, Weights Biases, FAISS

AWS EKS, Sqlalchemy, Celery, FastAPI, Kubernetes, Python, Airflow, pgvector, embedding models, Pinecone, confidence calibration techniques, LLM output validation frameworks, RAG context, Pydantic, Weaviate, Claude API, semantic search

amazon sqs , snowflake , Golang, Cassandra, Grafana, Sql, Tensorflow, New Relic, Nosql, Rabbitmq, Numpy, Postgres, Databricks, Python, Restful Services, AWS, GenAI, MLlib, NLP libraries, ScikitLearn, Agentic AI, Apache Kafka MSK

Data Science, Python, LangGraph, Statistical Modeling, Agentic Workflows using LangChain-based agents, Transformer architecture, Fine-tuning techniques for LLMs

workflow engines , Python, tool function calling, tracing observability tooling, Logfire, AI systems in production, prompting, Temporal, OpenTelemetry, pydantic-ai, LiteLLM, MCP, agent architectures

data engineering , Ml, FastAPI, Kubernetes, Python, AWS EKS, Nlp, Celery, Sqlalchemy, confidence calibration techniques, Airflow, Claude API, vector database, Ai, Pydantic, LLM output validation frameworks, embedding models, Weaviate, pgvector, semantic search, data ingestion pipelines, Pinecone, LLM evaluation frameworks, prompt engineering