software engineering practices , Apis, MLops, Microservices, Python, DataOps, LLMOps, architecture patterns, AI architectures, prompt engineering, cloud-native development

Numpy, Pandas, Gcp, FastAPI, Azure, Python, AWS, GenAI, embeddings, LLMs, prompt engineering, RAG

Nosql, Java, Restful Apis, Kubernetes, Python, Sql, AI productivity tools, Go, AI ML technologies

Java, Git, Machine Learning, Gcp, MLops, Docker, Pyspark, Azure, Python, Sql, AWS

Algorithms, Java, C, data mining, Tensorflow, Pytorch, Gcp, Docker, Linux, System Design, data structures, Rest Apis, Azure, Kubernetes, Python, AWS, Visualization, scikit-learn, Go, Statistics

data engineering , Pyspark, Sql, Artificial Intelligence, Advanced Prompt Engineering, Monitoring LLMOps, Full-stack AI Integration, Multi-Agent System Design, LLM Observability

Sqlalchemy, API security, PostgreSQL, Rabbitmq, Django, Sqlite, Flask, FastAPI, Python, Django ORM, LangChain, AI ML development, document extraction, langFuse, large-scale RAG systems, Cloud Data Warehouses, OCR pipelines, langSmith, RAG methodologies, LangGraph, Java Spring Boot, fine-tuning LLMs

Java, Python, LangChain, multi-agent systems, scalable AI infrastructure, streaming LLMs, inference optimization, Go, LlamaIndex