I'm a System Design Engineer 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.

Currently focused on:

• 🤖 Generative AI & Agentic AI – Building intelligent agents with LangChain & LangGraph
• 🧠 Deep Learning & Data Science – End-to-end ML pipelines, custom architectures, and advanced modeling
• 🏗️ System Design & DSA – Designing scalable distributed systems and solving algorithmic challenges
• 🔄 DevOps & MLOps – CI/CD automation, containerization, model deployment, and production monitoring
• 🔓 Open Source – Contributing to LLM tooling, agent frameworks, and AI infrastructure

Mission: Build next-generation AI systems and agents that are reliable, interpretable, scalable, and production-ready.

• LeetCode: 1800+ Rating | Data Structures & Algorithms Expert
• Codeforces: 1050+ Rating | System Design & Optimization
• HackerRank: Problem Solver | Algorithm & Data Structures
• National Achievement: 2nd Rank - ISRO Robotics Science Fair (2022)
• Key Strengths: Array/String | Trees/Graphs | Dynamic Programming | Greedy | Binary Search | System Design

Python | PyTorch | TensorFlow | Transformers | Hugging Face | JAX | ONNX | Model Optimization

Expertise in building and fine-tuning state-of-the-art language models, working with transformer architectures, and leveraging pre-trained models for production applications.

LangChain | LangGraph | AutoGen | LlamaIndex | Semantic Kernel | Tool Use | Agent Orchestration

Designing multi-agent systems with complex reasoning loops, tool-calling capabilities, and autonomous decision-making frameworks. Expertise in orchestrating agent interactions and building hierarchical AI systems.

CNNs | RNNs | LSTMs | Attention Mechanisms | Vision Transformers (ViT) | BERT/GPT Architectures | Custom Models

Building and implementing advanced neural network architectures for computer vision, NLP, and multimodal tasks. Proficient in architecture design, hyperparameter tuning, and model optimization.

GPT Models | Claude | Llama | Mistral | Prompt Engineering | Fine-tuning | RAG Systems | In-context Learning

Working with cutting-edge LLMs through APIs and local deployment. Expertise in prompt engineering, chain-of-thought reasoning, and building RAG systems for context-aware responses.

MLflow | Weights & Biases | DVC | Model Registry | Experiment Tracking | Feature Stores | Model Serving | Pipeline Orchestration

End-to-end ML lifecycle management including experimentation, versioning, deployment, and monitoring of machine learning systems at scale.

Python | PyTorch | TensorFlow | JAX | Transformers | Hugging Face | RAG | Vector Databases | Prompt Engineering | Fine-tuning | LLMs | Computer Vision | NLP

LangChain | LangGraph | OpenAI API | Anthropic Claude | Ollama | LlamaIndex | AutoGen | Semantic Kernel

Pandas | NumPy | Scikit-learn | Seaborn | Matplotlib | Plotly | XGBoost | Statistical Analysis | A/B Testing

Node.js | Express.js | Python | FastAPI | Django | REST APIs | GraphQL | WebSockets | gRPC

React.js | Next.js | Vue.js | TypeScript | Tailwind CSS | Framer Motion | Next UI | Responsive Design

Docker | Kubernetes | GitHub Actions | GitLab CI | Terraform | CI/CD Pipelines | MLflow | Weights & Biases | DVC | Linux/Bash | SSH | AWS | GCP | Azure

PostgreSQL | MySQL | MongoDB | Redis | Elasticsearch | Apache Spark | Apache Kafka | Data Pipelines

Python | JavaScript/TypeScript | C++ | Go | SQL | Bash | Java

Git | GitHub | GitLab | Jupyter Notebooks | VS Code | Docker Desktop | Vercel | Hugging Face Hub | Weights & Biases | Figma

• 🔗 LangChain Development

  • Building LLM-powered applications with chains and memory management
  • Creating document loaders, text splitters, and embedding pipelines
  • Implementing conversation buffers and persistent context management
  • Integrating multiple LLM providers (OpenAI, Claude, Ollama, Cohere)

• 📊 LangGraph Workflows

  • Designing complex multi-agent systems with state graphs
  • Creating deterministic workflows with branching logic and loops
  • Tool-calling agents with error recovery and retry mechanisms
  • Distributed agent coordination and inter-agent communication

• 🎯 Production-Ready Solutions

  • Deploying LangChain/LangGraph apps to production with FastAPI/Flask
  • Monitoring agent performance and prompt effectiveness
  • Scaling applications with caching and rate limiting
  • Building conversation UI/UX with streaming responses

• Agentic Workflows – Multi-step reasoning agents with LangChain & LangGraph
• RAG Systems – Retrieval-Augmented Generation for context-aware responses
• LLM Fine-tuning – Custom model adaptation for domain-specific tasks
• Prompt Engineering – Advanced techniques for optimal model performance
• Multimodal AI – Vision-language models and cross-modal understanding

• LangChain Mastery – Building production-grade LLM applications with memory, chains, and agents
• LangGraph Workflows – Creating complex multi-step agentic AI workflows with state management
• RAG Pipelines – End-to-end Retrieval-Augmented Generation with vector stores and semantic search
• Custom Agents – Tool-use agents, reasoning loops, and autonomous decision-making systems
• Integration Patterns – Seamless integration with OpenAI, Anthropic Claude, Ollama, and custom LLMs
• Production Systems – Deploying scalable LLM applications with error handling and monitoring

• Building scalable microservices and distributed systems
• API gateway patterns and load balancing strategies
• Database optimization and query performance tuning
• Caching strategies (Redis, Memcached) and CDN integration
• Event-driven architectures and message queues

• End-to-end ML pipeline development
• Model versioning, experimentation, and deployment
• Data engineering and ETL workflows
• Production monitoring and model drift detection
• Feature engineering and data preprocessing at scale

• Containerization and orchestration with Docker & Kubernetes
• CI/CD pipeline automation and best practices
• Infrastructure as Code (Terraform, CloudFormation)
• Monitoring, logging, and alerting systems (Prometheus, Grafana, ELK)
• Cloud deployment across AWS, GCP, and Azure

• Contributing to LangChain, LangGraph, and AI agent frameworks
• Building AI/ML tools for broader community adoption
• Knowledge sharing through Kaggle notebooks and technical writeups
• Mentoring and collaborative development

• 📚 Continuous Learner – Actively exploring latest developments in AI, System Design, and DevOps
• 🔍 Research-Oriented – Implementing cutting-edge techniques from recent papers and preprints
• 📝 Knowledge Sharing – Creating Kaggle notebooks, technical blogs, and LeetCode solutions
• 🏗️ Production Focus – Building systems that scale and perform reliably in real-world scenarios
• 🤝 Community Driven – Contributing to and learning from open-source communities

I'm always interested in:

• 🤝 Collaborating on AI/ML and system design projects
• 💬 Discussing competitive programming strategies
• 🔄 Contributing to open-source AI tooling
• 📚 Sharing knowledge and learning together
• 🚀 Building scalable systems and intelligent agents

Reach out via:

• 📧 LinkedIn: arun-kumar-giri-54a0b7318
• 🐍 LeetCode: ArPriCode
• 📊 Kaggle: arunkumargiri
• 💻 GitHub: ArPriCode
• ✍️ Medium: arun96