Retrieval Augmented Generation (RAG, Large Language Models (LLMs, Python, Sql, Clustering, Java, Tensorflow, MLops, Pytorch, Predictive Modeling, XGBoost, ranking, CI/CD, Fraud Detection, Go, Scikit-learn, vector databases, embeddings, Classification, Forecasting, recommendation systems

MLops, Python, Sql, tree-based models, classical ML regression, feature engineering, time-series forecasting, ML models, Validation, LLM agentic systems

Git, Python Programming, Lang Chain and Lang Graph, Experience with Lang fuse, Understanding of agentic design patterns and workflow orchestration, Working with Databricks and modern data platforms, Prompt engineering and context engineering, RAG embeddings semantic search

Azure), Large Language Models (LLMs), Cloud Platforms (AWS, Tensorflow, Pytorch, Python, Machine Learning, Gcp, CrewAI, PhiData, Autogen, Langgraph, Agentic AI Workflows, Generative AI, LLM Fine-Tuning, Multi-agent Systems, reinforcement learning, Prompt Engineering

FastAPI, Java, C++, Data Structures, Redis, Django, Algorithms, Gcp, Flask, MongoDB, Python, AWS, LLM-based systems, Hugging Face, Go, Object-oriented programming, OpenAI API, CI/CD, SQL databases, AI-assisted coding tools, Gemini

Machine Learning, Databricks, Python, Sql, Deep Learning, Graph Modelling, Knowledge Graphs, Identity Access Graph

data engineering , Machine Learning (ML), Python, Nlp, Machine Learning Algorithms, Architectural Patterns for Large-Scale Software Applications

data wrangling , Scikit, Github, Sql, Cuda, Tensorflow, Nltk, Numpy, Git, Pandas, Gcp, Spark, Keras, Python, AWS, Deep learning architectures, Testing metrics, Feature engineering, R, cuDNN, Software engineering fundamentals, Data query and data processing