class AkashJha:
    def __init__(self):
        self.name = "Akash Jha"
        self.role = "Data Scientist"
        self.location = "Gurugram, HR"
        self.experience = "1+ year"
        self.current_company = "ASPL"
        
    def get_skills(self):
        return {
            "AI/ML": ["LLMs", "RAG", "LangChain", "LangGraph", "PyTorch", "Scikit-Learn"],
            "GenAI": ["Gemini", "OpenAI", "AI Agents", "Fine-tuning"],
            "MLOps": ["Docker", "FastAPI", "AWS", "Model Monitoring", "CI/CD"],
            "Data": ["Python", "SQL", "FAISS", "Pinecone", "ETL", "Big Data"],
            "Viz": ["Power BI", "Tableau", "Streamlit", "Plotly"],
            "Tools": ["Git", "Jupyter", "VS Code", "MLflow", "Airflow"]
        }
    
    def get_current_focus(self):
        return [
            "🤖 Building AI-powered prescription alerting systems",
            "⚡ Optimizing real-time inference pipelines",
            "📊 Implementing semantic search with vector databases",
            "🔍 Reducing AI hallucinations with RAGAS evaluation"
        ]

    def say_hi(self):
        print("Thanks for dropping by! Let's build something amazing together 🚀")

me = AkashJha()
me.say_hi()

High-throughput streaming pipeline processing millions of data points daily

🔧 Tech Stack:

• Streaming: Apache Kafka, Docker
• Processing: Python, Pandas, NumPy
• Storage: PostgreSQL, Redis
• Monitoring: Grafana, Prometheus

📊 Performance:

• 10M+ daily data points processed
• 40% efficiency improvement
• Real-time insights generation
• 99.9% uptime reliability

Comprehensive ETL pipeline analyzing 39M housing records with interactive dashboards

🔧 Tech Stack:

• Cloud: AWS (S3, Lambda, EC2)
• Data: Snowflake, Apache Airflow
• Visualization: Power BI, Python
• Processing: Pandas, SQL

📊 Scale:

• 39M housing records processed
• 10-year price trend analysis
• Interactive Power BI dashboard
• Automated daily updates

• 🚨 AI Prescription Alerting System - LightGBM + LLM semantic clustering (30% manual intervention reduction)
• 🤖 GenAI Customer Service Bot - LangChain + RAG automation (80% query automation, 92% accuracy)
• 💰 Credit Risk Scoring Engine - ML-based eligibility system (18% default rate reduction)
• 🔍 Semantic Search Pipeline - OCR + FAISS + Pinecone (31% hallucination reduction)

• ⚡ Real-time Data Pipeline - Kafka + Docker processing 10M+ daily streaming data points (40% efficiency gain)
• 🏠 Redfin Housing Analytics - AWS + Snowflake + Airflow ETL pipeline for 39M housing records
• 💰 Fraud Detection System - ML models on 7M+ transactions with 15% accuracy improvement
• 🎬 Movie Recommendation Engine - NLP-based content filtering and user preference matching

timeline
    title Career Timeline
    
     3 Months : ML Intern
              : Internship Studio
              : Credit Scoring & Risk Models
              : LightGBM + SMOTE Implementation
    
     3 Months : Data Science Intern
              : Zidio Development
              : Build, Trained, and Integrated ML models.
              : GenAI Chatbots & RAG Systems
    
    2025-Present : Data Scientist
                 : ASPL
                 : Turning Data Into Decisions.
                 : Building, Training, and integrating ML models.

• 🤖 Building AI systems that actually solve real problems
• ⚡ Passionate about making AI faster and more reliable
• 📊 Love turning messy data into actionable insights
• 🔧 Always tinkering with new ML frameworks and tools
• 📚 Continuous learner - currently exploring multimodal AI
• 🎯 Goal: Make AI accessible and beneficial for everyone