🧊 spatial_telemetry.sh — 3D Core & System Telemetry (Click to toggle)
I engineer at the intersection of 3D Spatial Computing & Computer Graphics, Scalable Cloud MLOps on AWS, and Autonomous Multi-Agent Artificial Intelligence.
- 🧊 Procedural 3D Graphics & Spatial Computing: Developing programmatic 3D generative pipelines in Blender using the Python
bpyAPI. Engineering intricate parametric assemblies (such as Formula 1 internal kinetic powertrains), architectural spatial models (NIT Rourkela 3D digital twins), photorealistic Cycles lighting setups, and real-time interactive Three.js / WebGL viewports with custom shader materials. - ☁️ Cloud Infrastructure & AWS MLOps: Architecting resilient, high-throughput cloud environments on Amazon Web Services (AWS). Leveraging AWS SageMaker for scalable model training and distributed endpoints, AWS Lambda for event-driven serverless compute, Amazon EC2 & S3 for heavy neural workloads and asset pipelines, containerized with Docker and orchestrated over Kubernetes clusters with automated CI/CD.
- 🤖 Autonomous Multi-Agent AI & Neural Systems: Designing resilient agentic graphs using LangGraph, CrewAI, and LangChain. Formulating multi-agent state machines with memory persistence, self-correcting evaluation loops, and low-latency vector embeddings across Pinecone and ChromaDB.
- ⚡ High-Throughput Microservice Backends: Constructing asynchronous FastAPI architectures with rigorous Pydantic schema validation, low-latency relational engines (MySQL), and high-performance algorithms in Python and C++.
🧊 3D Modeling, Procedural Engineering & Spatial Graphics
- Procedural CAD & Animation: Programmatic mesh generation via Blender
bpy, complex hierarchical assemblies, keyframe interpolation, geometry nodes, and procedural texture baking. - Real-Time 3D WebGL: Interactive in-browser 3D viewports powered by Three.js, PBR material shading, dynamic lighting, OrbitControls, and exploded component disassembly.
- Rendering Engines: High-fidelity raytraced rendering using Cycles (OptiX / CUDA) and real-time rasterization with EEVEE Next.
☁️ Cloud Infrastructure, MLOps & Containerization (AWS Powered)
- Cloud Architecture: Elastic compute instances with AWS EC2, scalable object and model weight repositories with Amazon S3, serverless event-driven processing via AWS Lambda, and end-to-end MLOps management via AWS SageMaker.
- Orchestration: Multi-stage production builds with Docker, resilient multi-replica auto-scaling on Kubernetes, automated testing and continuous integration via GitHub Actions.
🤖 Generative AI & Autonomous Agentic Workflows
- Agentic Frameworks: Cyclic state machine coordination with LangGraph, role-specialized swarms using CrewAI, persistent memory architectures, and semantic retrieval indexing with LlamaIndex.
🧠 Deep Learning, Computer Vision & NLP
⚡ Backend, High-Performance APIs & Vector Engines
🎓 Academic Credentials & Specializations (Click to expand)
- National Institute of Technology, Rourkela
- B.Tech in Artificial Intelligence (2024 – 2028)
- Core Disciplines: Data Structures & Algorithms, OOP in C++, Artificial Intelligence & ML, Database Management Systems (SQL), Linear Algebra.
- Deep Learning Specialization — DeepLearning.AI & Coursera (2025)
- Neural Networks, Hyperparameter Optimization, Structuring ML Projects, CNNs, Sequence Models & Attention Mechanisms.
- Machine Learning Specialization — Stanford University & DeepLearning.AI (2025)
- Supervised Learning, Advanced Learning Algorithms, Unsupervised Learning & Recommender Systems.
- Leadership & Open Source:
- Technical Member, OpenCode Club (NIT Rourkela): Coordinated campus technical hackathons, conducted workshops on Git/GitHub workflows, procedural design, and deep learning.
- Sponsorship Committee, NITRUTSAV 2026: Led outreach and corporate sponsorship acquisition.
⚡ "Rendering reality through procedural dimensions, autonomous intelligence, and cloud-scale architectures."
{ "system": "Smit-Neural-3D-Kernel", "architect": "Smit Pathak", "institution": "National Institute of Technology, Rourkela", "degree": "B.Tech in Artificial Intelligence (2024 - 2028)", "specializations": { "3d_spatial_computing": ["Blender Procedural (bpy)", "Three.js", "WebGL", "Cycles/EEVEE PBR", "Kinematic Assemblies"], "cloud_infrastructure": ["Amazon Web Services (AWS)", "AWS EC2", "AWS S3", "AWS Lambda", "AWS SageMaker", "ECS/EKS"], "intelligent_systems": ["Autonomous Multi-Agent Swarms", "LangGraph", "Deep NLP", "PyTorch Neural Inference"] }, "render_pipeline": { "viewport": "Cycles OptiX Raytracing & Real-time WebGL Shaders", "deployment": "Containerized Microservices on Kubernetes & AWS Cloud" }, "current_focus": "Merging generative multi-agent systems with real-time procedural 3D graphics & cloud-scale MLOps", "telemetry_status": "🟢 ONLINE | Accepting high-impact AI/ML, 3D & Cloud engineering collaborations" }