, Natural Language Processing, C, Java, Machine Learning, Python, AI frameworks such as PyTorch, Agentic frameworks, R, reinforcement learning, Audio Processing, Generative AI models, Recommendation Systems

Pytorch, FastAPI, Python, Tensorflow, Gradio, Data preprocessing, Model evaluation techniques, Feature engineering, LangChain, MLOps practices, Streamlit, Scikit-learn

S3, Data Factory, ELT, Tensorflow, Deep Learning, Nlp, Pytorch, Python, AWS, Apache Spark, Emr, Gcp, Data Warehousing, DataFlow, Azure, Etl, Hugging Face, Pinecone, HDInsight, ChromaDB, RAG architectures, LangChain, Generative AI, LLMs, FAISS, LlamaIndex

System Design, Application Development, Python, Testing, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems, Machine Learning models

PostgreSQL, Docker, FastAPI, Kubernetes, Python, LangChain, model fine-tuning, agent memory systems, pgvector, RAG pipeline architecture, LLM evaluation and observability practices, financial close Record-to-Report accounting, prompt lifecycle management, platform-level engineering standards, Azure OpenAI, LLM engineering, LangGraph, context management strategies, Ragas, RLHF

System Design, Application Development, Testing, Python, Cloud-native deployment patterns, Operational stability for ML or data-intensive systems

Pytorch, Python, Hugging Face, Experimental Design, evaluation and benchmarking of ML systems, inference cost performance and optimisation techniques, Large Language Models