Hi there, I'm Gopal Padhi 👋

Machine Learning Engineer | AI Solutions Architect | Agentic AI Developer

🚀 About Me Senior ML Engineer building AI-powered solutions for manufacturing analytics and enterprise intelligence. I specialize in agentic AI systems, knowledge graphs, and transforming complex data into production-ready ML platforms. 🤖 Agentic AI Development: Building autonomous agents with reasoning, planning, and tool use capabilities 🧠 Knowledge Graph AI: Creating universal intelligence layers connecting manufacturing data, processes, and insights 🔬 Vision Language Models: Exploring Qwen2.5-VL for document extraction and multimodal understanding 🏭 Production Systems: Manufacturing analytics dashboards, OEE optimization, predictive maintenance 🎮 Side Quest: Avid gamer with a high-end streaming setup 💬 Let's talk: Agentic AI, RAG systems, Knowledge Graphs, MLOps, or your next ML challenge

class CurrentWork:
    def __init__(self):
        self.role = "Senior ML Engineer"
        self.focus = "Agentic AI & Knowledge Graph Systems"
        
    def active_projects(self):
        return {
            "agentic_ai": {
                "systems": "Smart AI agents with reasoning & planning",
                "rag_architecture": "Advanced retrieval systems with multi-hop reasoning",
                "tools": "LangChain, LlamaIndex, custom agent frameworks",
                "capabilities": "Autonomous decision-making, tool orchestration"
            },
            "knowledge_graphs": {
                "project": "Universal Manufacturing Intelligence Layer",
                "description": "Graph-based knowledge system connecting processes, metrics, and insights",
                "stack": "Neo4j, NetworkX, custom graph embeddings",
                "use_cases": "Context-aware querying, relationship discovery, semantic search"
            },
            "ai_research": {
                "models": "Qwen2.5-VL, LLaMA, Claude",
                "tasks": "Document extraction, fine-tuning, multimodal understanding",
                "deployment": "Production-grade inference optimization"
            },
            "manufacturing_analytics": {
                "dashboards": "Apache Superset with intelligent alerting",
                "metrics": "OEE tracking, downtime analysis, predictive maintenance",
                "automation": "AI-driven anomaly detection & forecasting"
            }
        }
    
    def get_expertise(self):
        return [
            "Agentic AI Systems & Autonomous Agents",
            "Retrieval-Augmented Generation (RAG)",
            "Knowledge Graph Engineering",
            "Vision Language Models & Fine-tuning",
            "Manufacturing Analytics & Time Series",
            "MLOps & Production Deployment"
        ]

Bridging the gap between cutting-edge AI research and real-world production systems:

• Agentic AI: Multi-agent systems, reasoning frameworks, tool use, planning algorithms
• Knowledge Graphs: Graph neural networks, semantic reasoning, intelligent data connectivity
• Advanced RAG: Multi-hop retrieval, hybrid search, context optimization, query understanding
• Vision Language Models: Document understanding, OCR alternatives, multimodal reasoning
• Time Series at Scale: Forecasting, anomaly detection, pattern recognition in manufacturing
• MLOps Excellence: Deployment strategies, model monitoring, automated retraining pipelines

🤖 Agentic AI Development

• Designing autonomous agents that reason, plan, and execute complex tasks
• Building RAG systems with advanced retrieval strategies and context management
• Creating tool-using agents that integrate with external APIs and databases

🧠 Knowledge Graph Intelligence

• Architecting graph-based knowledge systems for manufacturing domains
• Implementing semantic search and relationship discovery across complex data
• Building the foundation for context-aware AI that understands connections

🏭 Manufacturing AI

• Production-grade analytics dashboards with real-time monitoring
• Time series forecasting models for predictive maintenance
• Automated quality control using computer vision and anomaly detection

💡 Innovation & Deployment

• Fine-tuning large language models for specialized domains
• Optimizing inference for production at scale
• Rapid prototyping and iterative development of AI solutions

Python       ████████████████████░░░░░   85%   (AI/ML, Automation, Data Science)
Jupyter      ███████░░░░░░░░░░░░░░░░░░   28%   (Research, Experimentation)
JavaScript   ████░░░░░░░░░░░░░░░░░░░░░   15%   (Web Development, UI/UX)
TypeScript   ███░░░░░░░░░░░░░░░░░░░░░░   12%   (Full-stack Applications)
HTML/CSS     ██░░░░░░░░░░░░░░░░░░░░░░░   08%   (Frontend Development)

• Research & Prototyping: Agentic AI systems, RAG architectures, Knowledge Graphs
• Production Work: Manufacturing analytics, ML pipelines (in private repositories)
• Open Source: AI agent experiments, VLM fine-tuning, automation tools
• Active Areas: Multi-agent systems, LangChain, PyTorch, Hugging Face

Always interested in discussing AI agents, knowledge graphs, RAG architectures, or collaboration on challenging ML problems.

Professional

• 📧 Email: gopalpadhi8@hotmail.com
• 💼 LinkedIn: gopal-padhi

Community

• 🐦 Twitter: @i_am_gops_
• 📊 Kaggle: gopalpadhi