🚀 Arun Kumar Giri
Competitive Programmer | AI/ML & Generative AI | Agentic AI | Deep Learning | DevOps/MLOps | Open Source
Competitive Programmer | AI/ML & Generative AI | Agentic AI | Deep Learning | DevOps/MLOps | Open Source
I'm a and AI/ML specialist passionate about building scalable systems, architecting intelligent solutions, and contributing to open-source AI tooling. With expertise spanning competitive programming, deep learning, and DevOps/MLOps, I bridge the gap between cutting-edge AI research and production-grade systems.
Artificial Intelligence and Machine Learning are at the core of modern technology. This field covers supervised and unsupervised learning, model training, feature engineering, and deployment. I specialize in building robust ML pipelines, optimizing models, and applying advanced algorithms to solve real-world problems.
Key Tools: Python, PyTorch, TensorFlow, Scikit-learn, JAX, Hugging Face.
Generative AI focuses on creating new content, data, or solutions using models like LLMs, GANs, and VAEs. I work on prompt engineering, fine-tuning large language models, and building RAG systems for context-aware generation. My expertise includes multimodal AI and deploying generative models for production use.
Key Tools: GPT, Claude, Llama, Mistral, Transformers, Hugging Face.
Agentic AI is about designing autonomous agents capable of complex reasoning, tool-calling, and multi-agent orchestration. I build systems using LangChain, LangGraph, and other frameworks to enable agents that interact, collaborate, and make decisions independently. This includes hierarchical agent design and advanced orchestration patterns.
Key Frameworks: LangChain, LangGraph, AutoGen, LlamaIndex, Semantic Kernel.
Deep Learning powers breakthroughs in computer vision, NLP, and analytics. I design and implement neural architectures (CNNs, RNNs, LSTMs, Attention), handle large datasets, and visualize results for actionable insights. My work includes model optimization, hyperparameter tuning, and deploying scalable solutions.
Key Tools: PyTorch, TensorFlow, Pandas, NumPy, Seaborn, Matplotlib, Plotly, XGBoost.
DevOps and MLOps ensure reliable, scalable, and automated deployment of AI/ML systems. I implement CI/CD pipelines, containerization, orchestration, and monitoring for production-grade models. My expertise includes experiment tracking, model registry, and cloud infrastructure management.
Key Tools: Docker, Kubernetes, GitHub Actions, GitLab CI, Terraform, MLflow, Weights & Biases, DVC, Linux/Bash, AWS, GCP, Azure.
Open Source is central to innovation and collaboration. I actively contribute to AI/ML libraries, agent frameworks, and infrastructure tools. My work supports the broader community, drives adoption, and accelerates progress in AI.
Key Platforms: GitHub, Hugging Face Hub, LangChain, LangGraph, DVC, MLflow.
Supervised learning is a machine learning paradigm where models are trained using labeled data. The algorithm learns to map inputs to outputs based on example pairs, enabling tasks like classification and regression. Common algorithms include decision trees, support vector machines, and neural networks.
Unsupervised learning deals with unlabeled data, aiming to discover hidden patterns or groupings. Clustering (like K-means) and dimensionality reduction (like PCA) are key techniques. This approach is used for anomaly detection, data exploration, and feature extraction.
Neural networks are computational models inspired by the human brain, consisting of interconnected layers of nodes (neurons). They excel at learning complex patterns and are foundational to deep learning. Multi-layer perceptrons (MLPs) are the simplest form, while advanced architectures include CNNs and RNNs.
MLPs are feedforward neural networks with multiple layers of neurons. They are used for tasks like classification and regression, learning non-linear relationships in data. MLPs are the building blocks for more complex deep learning models.
CNNs are specialized neural networks for processing grid-like data, such as images. They use convolutional layers to extract spatial features, making them ideal for computer vision tasks like image classification, object detection, and segmentation.
RNNs are designed for sequential data, maintaining memory of previous inputs. They are widely used in natural language processing (NLP), time series analysis, and speech recognition. Variants like LSTM and GRU address issues like vanishing gradients.
RNNs power many NLP applications, including language modeling, text generation, and machine translation. Their ability to capture temporal dependencies makes them effective for understanding context in sequences of words.
Linear regression is a fundamental statistical method for modeling the relationship between a dependent variable and one or more independent variables. It is used for prediction and trend analysis, forming the basis for many machine learning algorithms.
Transfer learning leverages knowledge from pre-trained models to solve new, related tasks. This approach accelerates training, improves performance, and is especially powerful in deep learning, where models like BERT and GPT are fine-tuned for specific applications.
Clustering is an unsupervised technique for grouping similar data points. Algorithms like K-means, DBSCAN, and hierarchical clustering are used for market segmentation, image analysis, and anomaly detection.
Bias refers to systematic errors in machine learning models, often caused by assumptions in the learning algorithm or data. Addressing bias is crucial for building fair, accurate, and generalizable models.
Large Language Model (LLM) architectures, such as GPT and BERT, use deep neural networks to process and generate human-like text. They employ attention mechanisms and transformer layers to capture context and relationships in language, enabling advanced NLP tasks.
Transformers are a revolutionary deep learning architecture for sequence modeling. They use self-attention to weigh the importance of different input elements, enabling parallel processing and superior performance in NLP, vision, and multimodal tasks. Transformers underpin models like GPT, BERT, and Vision Transformers (ViT).
Concept: A supervisor agent that ensures enterprise AI compliance and safety.
The Problem: AI models can "drift," hallucinate, or exhibit bias without constant oversight.
Agentic Actions: