Data Scientist | AI/ML Engineer | GeN AI | Agentic AI Specialist
🇮🇳 India | 💼 4+ Years Experience | 🚀 Building Production-Grade AI/ML Systems
🔬 Data Scientist (AI/ML Engineer) skilled in building, deploying, and optimizing end-to-end Machine Learning and Generative AI / Agentic AI solutions at scale.
- 📊 Handling 1PB+ large datasets and developing real-time data pipelines
- 🚀 Delivering production-grade AI/ML/GenAI/Agentic AI systems across cloud environments
- 🤖 Building GenAI LLM-based chatbots, vector search systems, and secure, scalable enterprise applications
- 🔧 API development & integration, automation, and data engineering workflows
- 📈 Breaking down complex problems, optimizing model performance, and driving measurable business outcomes
- Experience in diverse AI/ML algorithms: LR, SVM, Decision Trees, Random Forest, XGBoost, R-CNN, NLP
- Expertise in Computer Vision, Text Analytics, and business value analysis
- Strong background in algorithm design, model evaluation, error analysis
- Successfully handled petabyte-scale (1PB+) data in real-world environments
- Deployed ML/DL/CV/NLP/GenAI/Agentic AI models into production in collaboration with engineering teams
Supervised Learning:
Linear Regression • Logistic Regression • Decision Trees • Random Forest • SVM • Naive Bayes • k-NN • Gradient Boosting • XGBoost • LightGBM
Unsupervised Learning:
k-Means • Hierarchical Clustering • DBSCAN • PCA • t-SNE • Autoencoders • GMM
Other ML:
Reinforcement Learning • Time Series (ARIMA, SARIMA, Prophet) • Recommendation Systems
Architectures: • Transformers • CNN • RNN • LSTM • GANs • YOLOv8 • R-CNN
Applications: • OCR • Object Detection • Classification • Segmentation
Expertise:
- LLM Architectures • Prompt Engineering • RAG (Retrieval Augmented Generation)
- Fine-Tuning (SFT, LoRA, QLoRA) • Model Optimization & Quantization (GGUF, 4-bit/8-bit)
- OpenAI GPT Models • Llama3 • Mixtral • Claude (Anthropic) • Vertex AI
Capabilities: Embedding Models (OpenAI, HF, BGE, E5) • Hybrid Search (BM25 + Vector) • Reranking (Cohere/BGE) • Chunking Strategies
Frameworks: LangChain • LangGraph • LangFlow • CrewAI • PhiData (Agno) • OpenAI Agents SDK • Autogen • LlamaIndex • MCP (Client/Server) • LangSmith
Capabilities: Tool Calling • Memory Systems • Multi-Agent Workflows • Agent Orchestration • FastAPI Integrations
Core: S3 • EC2 • Lambda • IAM • CloudWatch
AI/ML: SageMaker • Bedrock • Kendra • Guardrails
Data: EMR • DynamoDB • Redshift • Glue • Athena • OpenSearch
Integration: API Gateway • SNS • SQS • ECS
Analytics: QuickSight • CloudFormation • Cognito
Tools: AWS Ecosystem • MLFlow • Docker & DockerHub • Jenkins • Git/GitHub • GitLab • CI/CD • Streamlit • Pytest • Jira
Concepts: High Level Design (HLD) • Low Level Design (LLD) • Scalability • CAP Theorem • Sharding • Caching • Load Balancing
Developed and integrated RESTful APIs using FastAPI and FlaskAPI
| Duration | Institute | Degree & Specialization | GPA / CGPA | Links |
|---|---|---|---|---|
| Aug 2022 - 2024 | Woolf University | Master of Science (MS) Computer Science: Artificial intelligence and Machine Learning |
4.0 / 4.0 GPA | College | Degree | Academic institution |
| Jul 2016 - 2020 | Guru Nanak Dev Engineering College | Bachelor of Technology (B.Tech) Information Technology |
7.34 / 10.0 CGPA | College | Degree |
| When | Where | Designation | Key Responsibilities | Project Details | Links |
|---|---|---|---|---|---|
| Dec 2022 - Jan 2026 | TCS | Data Scientist (AI/ML Engineer) |
• AI/ML Use Cases | - | [Link] |
| Dec 2021 - Dec 2022 | TCS | Data Engineer & Analyst | • Data Engineering AWS Stack • Business Intelligence • Dashboard Reports |
- | [Link] |
| Section | Topics | Sub-Topics | Sub Topics In Details | Project Details (Use Cases), Tech Stack & Links |
|---|---|---|---|---|
| A | Agentic AI & Gen AI | 1. Agentic AI & MCP 2. Gen AI, RAG, LLM 3. AIOps, LLMops using AWS services 4. UI/UX → Streamlit, ReactJS |
1. Agentic AI & MCP • Agentic AI: Autonomous agents that perceive, decide, and act (e.g., RPA, trading bots). • MCP: End-to-end framework for orchestration, versioning, A/B testing, and feedback loops. 2. Gen AI, RAG, LLM • Gen AI: Models generating text/images/code via massive unsupervised learning. • RAG: Enhances LLMs by fetching external knowledge to reduce hallucinations. • LLM: Transformer-based models (GPT-4, Llama) fine-tuned for specific tasks. 3. AIOps, LLMops (AWS) • AIOps: AI for IT ops (anomaly detection, root-cause analysis). • LLMops: Managing LLMs at scale (inference costs, latency). • AWS: SageMaker (End-to-end), Lambda (Serverless), CloudWatch (Monitoring). 4. UI/UX → Streamlit, ReactJS • UI/UX: Focuses on user‑centric design—intuitive interfaces and smooth experiences. • Streamlit: Python library that turns data scripts into shareable web apps instantly (great for ML demos). • ReactJS: JavaScript framework for building complex, state‑managed front‑ends with reusable components. |
•MCP & A2A 1.Weather Agent (MCP & Agent to Agent) •[Details] •[Tech Stack] •[Live Demo] •Agentic AI 1.Agentic AI Trip Planner (CrewAI)• [Details] • [Tech Stack] • [Live Demo] 2.Enterprise Multi-AI Agent Systems(LangGraph,Langchain,LlamaIndex) •[Details] •[Tech Stack] •[Live Demo] •RAG & LLM 1.Universal PDF RAG Chatbot • [Details] • [Tech Stack] • [Live Demo] 2.Medical RAG Chatbot (HuggingFace,Langchain,Llama3) •[Details] •[Tech Stack] •[Live Demo] •GeN AI 1.AI-Teaching-Assistant •[Details] 2.AI Enterprise Systems ChatGPT • [Details] 3.GenAI Music Composer(LangGraph,Langchain,LlamaIndex) •[Details] •[Tech Stack] •[Live Demo] •LLMOps & AIOPs 1.Flipkart-Product-Recommender-RAG •[Details]•[Tech Stack]•[Live Demo] 2.GenAI Music Composer(LangGraph,Langchain,LlamaIndex) •[Details] •[Tech Stack] •[Live Demo] |
| B | Deep Learning | 1. Neural Networks 2. Computer Vision 3. NLP (Natural Language Processing) 4. Transformer |
1. Neural Networks • Layers of interconnected neurons (input, hidden, output). • Forward pass computes outputs; backward pass (backpropagation) updates weights using gradients. 2. Computer Vision • CNNs (Convolutional Neural Networks): Extract spatial hierarchies in images via convolutions & pooling. • Architectures: YOLO (real‑time object detection), ResNet (deep networks with skip connections to avoid vanishing gradients). 3. NLP (Natural Language Processing) • Tasks: text classification, sentiment analysis, named‑entity recognition. • Techniques: word embeddings (Word2Vec, GloVe), sequence modeling (RNNs, LSTMs). 4. Transformer • Self‑attention mechanism that weighs input token relevance dynamically. • Enables parallel processing, improving performance on long sequences. • Variants: BERT (bidirectional, masked language modeling), GPT (generative, autoregressive). |
•Neural-Network 1.Neural Network Powered Delivery Time Estimation •[[Details] •Computer Vision 1.Tesla-Autonomous-Car-Driving-Vision-YOLOv5-Object-Detection • [Details]• [Tech Stack] • [Live Demo] 2.Defence AI: Multi-Sensor System • [Details]• [Tech Stack] • [Live Demo] 3.AI Driven Hotel Invoice Processing Pipeline •[Details] 4.Agri_Tech-AI-Powered-Vegetable-Classifier •[Details] •NLP 1.Twitter-NER-System •[Details] 2.FlipItNews-NLP-Classifier •[Details] 3.AI-Powered FullStack News Classifier •[Details]•[Tech Stack] •[Live Demo] 4.BERT embeddings with traditional NLP features •[Details] •Transformer 1.Fine-tuning Transformer Models Using PEFT (Parameter-Efficient Fine-Tuning) Techniques •[[Details] |
| C | Machine Learning | 1. Maths for ML (Probability, Stats, Algebra, Calculus) 2. ML Types (Supervised, Unsupervised, RL, Time Series) 3. MLOps + FastAPI + Docker + AWS services |
1. Maths for ML • Probability: Distributions (Gaussian, Bernoulli), Bayes theorem for probabilistic models. • Statistics: Hypothesis testing, confidence intervals, regression analysis. • Linear Algebra: Matrix operations, eigen‑decomposition for PCA/dimensionality reduction. • Calculus: Gradient descent (optimization), chain rule for backpropagation. 2. Machine Learning types • Supervised Learning: Labeled data; algorithms—linear regression, SVM, random forests. • Unsupervised Learning: Unlabeled data; clustering (k‑means), anomaly detection, PCA. • Reinforcement Learning: Agent learns via rewards/penalties; Q‑learning, Deep Q‑Networks (DQN). • Time Series & Recommendation: ARIMA forecasting, LSTM for sequences; collaborative filtering for recommendations. 3. MLOps + FastAPI + Docker + AWS • MLOps: ML DevOps—pipeline automation (CI/CD), model monitoring, reproducibility. • FastAPI: High‑performance Python web framework for building REST APIs (serving ML models). • Docker: Containerization packages code + dependencies for consistent environments. • AWS deployment: EC2 (VMs), Lambda (serverless), SageMaker (managed ML services). |
•Time Series & Forecasting 1.AdEase AI Forecasting Engine •[Details]•[Tech Stack]•[Live Demo] •Recommendation-System 2.ZeeMovies Movie Recommendation System•[Details]•[Tech Stack]•[Live Demo] •Manual Clustering(Unsupervised Clustering -K-means,Hierarchical Clustering) 3.EdTech Learner Clustering Analysis•[Details]•[Tech Stack]•[Live Demo] •Ensemble Learning-Bagging & Boosting,KNN Imputation of Missing Values,Random Forest,XGBoost,Working with an imbalanced dataset 4.OLA Driver Churn Prediction •[Details] •[Tech Stack] •[Live Demo] •Feature Engineering,Logistic Regression, Precision Vs Recall Tradeoff 5.LoanTap-Credit-Risk-Analysis •[Details] •[Tech Stack] •[Live Demo] •Exploratory Data Analysis,Linear Regression,Statsmodels 6.Jamboree Education-Linear Regression, •[Details] •[Tech Stack] •[Live Demo] •Feature Creation,Relationship between Features,Column Normalization/Column Standardization,Handling categorical values,Missing values-Outlier treatment/Types of outliers 7.Delhivery Logistics-Feature Engineering •[Details] •[Tech Stack] •[Live Demo] •Bi-Variate Analysis,2-sample t-test: testing for difference across populations,ANNOVA,Chi-square 8.Yulu Bike-Hypothesis Testing •[Details] •[Tech Stack] •[Live Demo] |
| D | Data Analyst | 1.Python & Libraries (NumPy, Pandas, EDA) 2.SQL 3.Statistics & Probability 4.Dashboard tools (Qlik Sense,Power BI,Tableau,Excel) |
1. Python & libraries • NumPy: Numerical operations on arrays. • Pandas: Data manipulation (DataFrame, cleaning, aggregation). • Matplotlib/Seaborn: Static & aesthetic visualizations. • SciPy: Scientific computing (optimization, stats). • EDA: Summarizing/visualizing data to find patterns or anomalies. 2. SQL • Querying: Joins, Window Functions, Aggregate functions. • Manipulation: DDL, DML, Indexing, and Optimization. 3. Probability & Statistics • Stats: Mean, Median, Mode, Standard Deviation, Hypothesis Testing. • Probability: Bayes Theorem, Distributions (Normal, Binomial), A/B Testing. 4. Dashboard tools • Power BI: Microsoft’s business analytics (drag‑and‑drop reports, DAX). • Tableau: Interactive visualizations & dashboards. • Qlik Sense: Associative analytics for data discovery. • Excel: PivotTables, Power Query for basic analytics. |
•Univariate & Bivariate & For continuous variable(s):Distplot,countplot,histogram for univariate analysis & For categorical variable(s):Boxplot & For correlation: Heatmaps,Pairplots 1.Walmart-Confidence Interval and CLT •[Details] •[Tech Stack] •[Live Demo] •EDA,correlations,outlier detection,segmentation,and 3D visualizations.using Python,Streamlit,Plotly,Pandas,NumPy,Seaborn & Matplotlib, Probability& Statistics. 2.Aerofit-Descriptive Statistics & Probability •[Details] •[Tech Stack] •[Live Demo] •Business-intelligence,EDA,using Python,Streamlit,Plotly,Pandas,NumPy,Seaborn & Matplotlib,content-strategy,Probability& Statistics. 3.Netflix-Data Exploration and Visualisation •[Details] •[Tech Stack] •[Live Demo] •SQL,DuckDB,comprehensive SQL-based insights,and dynamic Plotly visualizations for exploring sales trends,geography, logistics,and customer behavior. 4.Target Brazil E-Commerce Analytics Dashboard •[Details] •[Tech Stack] •[Live Demo] |
| E | Data Engineering | 1. Big Data (Spark, Hadoop, Airflow, Kafka, ETL) 2. Data Warehouse & Databases (SQL, NoSQL, Snowflake) 3. AWS Services |
1. Big Data • PySpark: Python API for Spark; handles large‑scale data processing. • Apache Spark: In‑memory computation engine for fast analytics (RDDs, DataFrames). • Hadoop: HDFS (storage) & MapReduce (batch processing). • Hive: SQL‑like queries over Hadoop data (data warehousing). • Airflow: Workflow orchestration (DAGs) for scheduling ETL jobs. 2. Data Warehouse & Databases • Data Warehouse: Optimized for read‑heavy analytics (schema‑on‑read, OLAP). Examples: Amazon Redshift, Snowflake. • NoSQL: Schema‑flexible databases—document (MongoDB), wide‑column (Cassandra). • SQL Database: Relational DBs (MySQL, PostgreSQL) for structured queries & transactions. 3. AWS services • S3: Object storage for raw data lakes. • Glue: Serverless ETL service for data preparation. • Redshift: Fully managed data warehouse for analytics. • EMR: Managed Hadoop/Spark cluster for big‑data processing. |
•Data Pipeline using Kafka 1.Realtime Telecom Data Pipeline Kafka•[[Details]•[Tech Stack]•[Live Demo] •Data Pipeline Airflow-Kafka-Spark-Cassandra-Docker 2.Realtime Data Pipeline-Airflow-Kafka-Spark-Cassandra-Docker•[[Details]•[Tech Stack]•[Live Demo] |
| F | Machine Learning & Data Engineering System Design | 1. High Level (HLD) & Low Level Design (LLD) 2. Scalability & Reliability (CAP, Load Balancing) 3. Distributed Systems & Microservices 4. Database Design (Sharding, Caching) |
1. High Level (HLD) & Low Level Design (LLD) • Every robust ML system (like a Recommendation Engine or Fraud Detection system) starts here. • Need HLD to map out how data flows from ingestion to training to inference, and LLD to design specific APIs. 2. Scalability & Reliability (CAP, Load Balancing) • Data Engineering deals with massive scale (Petabytes). • Understand how to scale horizontally (adding more servers) and ensure reliability when thousands of users hit your model. 3. Distributed Systems & Microservices • Big data tools (Spark, Kafka) are distributed systems. • Modern ML apps are built as microservices (e.g., separate services for data, model, UI) vs giant apps. 4. Database Design (Sharding, Caching) • Sharding: Essential when data is too big for one database (common in Data Eng). • Caching: Essential for low-latency ML inference (e.g., storing pre-calculated features in Redis). |
•Q&A Ranking System (Quora/Reddit/Facebook) and E-commerce Promotion Forecasting (Amazon/Flipkart) 1.Machine Learning Model for Q&A Ranking •[Details] •Airbnb Home Value Prediction - End-to-End ML System Design 2.Airbnb Home Value Prediction •[Details] •A complete machine learning system that predicts the next app a user will open on their iPhone with 90% accuracy and <100ms latency. •The Real-Time Fraud Analytics System is designed to detect fraudulent transactions in real-time for fintech applications.The system processes up to 10,000 transactions per second with sub-100ms latency. 3.Real-Time Fraud Analytics System •[Details] •A complete machine learning system that predicts the next app a user will open on their iPhone with 90% accuracy and <100ms latency. 4.AI Powered Next App Prediction •[Details] •Real-Time Data Streaming-Ingests and processes live stock data using Apache Kafka & Machine Learning Predictions-Uses an LSTM (Long Short-Term Memory) neural network to predict future stock prices in real-time. 5.Real-Time Stock Market Analysis System •[Details] •The Airline Ticket Shopping System is a comprehensive, production-ready ML platform built on AWS that enables airlines, travel agencies, and market. analysts to optimize pricing strategies, forecast demand, and provide personalized recommendations in real-time.Open-source ML community (XGBoost, scikit-learn, PySpark). 6.Airline ML Dynamic Pricing System •[Details] •Real-Time ETA Prediction System End-to-end ML system design for accurate food delivery time estimation using AWS services, advanced feature engineering, and gradient boosting models. 7.Food Delivery Order Real Time ETA ML Prediction System •[Details] •A comprehensive System Design and Prototype for a scalable,AI-driven photo organization platform similar to Google Photos.The complete Machine Learning System Design document.Includes architecture diagrams,component breakdown (Lambda, Rekognition, OpenSearch),and data flow strategies. 8.AI-Powered Photo Organizer •[Details] •Audio-Recognition-System Design ,The heart of the Shazam app is its ability to recognize songs through a process called audio fingerprinting.Similar to human fingerprints, each piece of music has a unique identifier that Shazam uses to identify songs from short audio snippets. 9.Audio-Recognition-System •[Details] |
| G | Competitive Programming | 1. Algorithms 2. Data Structures |
1. Problem‑solving frameworks • Understand constraints, optimize time/space complexity (Big O). • Techniques: Two Pointers, Sliding Window, Bit Manipulation, Recursion. 2. Algorithms • Core: Sorting (Merge/Quick), Searching (Binary Search). • Advanced: Dynamic Programming (DP), Greedy, Backtracking, Graph Algorithms (BFS/DFS, Dijkstra). 3. Data Structures • Linear: Arrays, Linked Lists, Stacks, Queues, Hash Maps. • Trees & Graphs: Binary Trees, BST, Heaps, Tries, Disjoint Sets. |
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A next-generation Agentic AI system that uses the Model Context Protocol (MCP) to standardize tool usage, enabling Llama 3 to fetch real-time global weather data with sub-second latency and zero hallucinations.
- Tech: MCP Server/Client • Llama 3 (Groq LPU) • LangChain • Streamlit • Open-Meteo • WebRTC
- Features: Universal tool protocol implementation, multi-modal voice interaction, sub-second reasoning, robust NLP city extraction, and interactive architectural visualization.
Multi-agent AI system for intelligent travel planning using CrewAI framework
- Tech: CrewAI • LangChain • Multi-Agent Orchestration
- Features: Automated itinerary generation, budget optimization, personalized recommendations
Advanced LangGraph-driven multi-agent ecosystem designed for high-speed intelligent reasoning and real-time orchestrated web research.
- Tech: LangGraph • Groq (Llama 3.1) • Tavily API • FastAPI • Docker • Jenkins • AWS
- Features: Cyclic agentic workflows • Near-zero latency inference • Automated DevSecOps/LLMOps/AIOps pipeline with SonarQube quality gates
Advanced RAG-based chatbot for PDF document Q&A
- Tech: LangChain • FAISS • OpenAI Embeddings • RAG
- Features: Multi-document support, semantic search, context-aware responses
A production-grade AI health assistant that delivers accurate, evidence-backed answers from medical encyclopedias using Retrieval-Augmented Generation (RAG) to eliminate hallucinations.
- Tech: LangChain • Llama 3 (HuggingFace) • FAISS • Streamlit • Docker • Jenkins • AWS App Runner • Aqua Trivy
- Features: Source-cited medical answers, sub-second vector retrieval, hallucination-free context injection, interactive system architecture visualization, and automated CI/CD deployment pipeline.
AI-powered recommendation engine using Retrieval-Augmented Generation for intelligent product discovery
- Tech: LangChain • Groq (Llama 3) • AstraDB • HuggingFace • Streamlit
- Features: Semantic search, review sentiment analysis, context-aware recommendations, real-time RAG pipeline
Production-grade generative AI orchestration studio that transforms natural language prompts into high-fidelity musical compositions using sub-second LLM inference.
- Tech: Groq LPU (Llama 3.1) • LangChain • Music21 • Synthesizer • Docker • GKE (Kubernetes)
- Features: Real-time melody & harmony orchestration, automated music theory validation, multi-tab operational monitoring, and professional WAV/MIDI export capabilities.
Real-time news classification using advanced NLP techniques
- Tech: BERT • Transformers • NLP • Classification
- Features: Multi-class news categorization, sentiment analysis
Named Entity Recognition system for social media text
- Tech: SpaCy • NER • NLP • Entity Extraction
- Features: Real-time entity detection, visualization, custom entity types
Real-time object detection for autonomous driving scenarios
- Tech: YOLOv5 • OpenCV • Computer Vision
- Features: Multi-object detection, real-time processing, bounding box visualization
Advanced surveillance system using YOLOv8
- Tech: YOLOv8 • Multi-sensor Fusion • Real-time Detection
- Features: Threat detection, multi-camera support, alert system
Advertising effectiveness forecasting using time series models
- Tech: ARIMA • SARIMA • Prophet • Time Series
- Features: Trend analysis, seasonality detection, future predictions
Student segmentation for personalized learning
- Tech: K-Means • DBSCAN • Clustering • Unsupervised Learning
- Features: Student profiling, learning pattern analysis, recommendations
Credit risk assessment and loan default prediction
- Tech: XGBoost • Random Forest • Classification • Risk Modeling
- Features: Credit scoring, risk stratification, feature importance analysis
Comprehensive logistics and supply chain analytics
- Tech: Pandas • Plotly • Data Visualization • Dashboard
- Features: Route optimization, delivery time prediction, KPI tracking
ML-based graduate school admission probability calculator
- Tech: Logistic Regression • Feature Engineering • Classification
- Features: Admission probability, university recommendations, profile analysis
Demand forecasting for bike-sharing services
- Tech: Time Series • Regression • Demand Forecasting
- Features: Hourly demand prediction, weather impact analysis, station optimization
Customer segmentation and product recommendation system
- Tech: Clustering • RFM Analysis • Customer Analytics
- Features: Customer profiling, product affinity, targeted marketing insights
Sales pattern analysis and revenue optimization
- Tech: EDA • Statistical Analysis • Visualization
- Features: Purchase behavior analysis, demographic insights, sales forecasting
Driver retention prediction using machine learning
- Tech: Gradient Boosting • Feature Engineering • Classification
- Features: Churn probability, retention strategies, driver profiling
Content analysis and recommendation insights
- Tech: NLP • Content Analysis • Recommendation Systems
- Features: Genre analysis, content trends, viewer preferences
E-commerce performance and customer behavior analysis
- Tech: SQL • Python • Business Intelligence
- Features: Sales metrics, customer lifetime value, product performance
Streaming data pipeline for telecom analytics
- Tech: Apache Kafka • Streaming • Real-time Processing
- Features: Live data ingestion, stream processing, real-time dashboards
End-to-end big data pipeline with orchestration
- Tech: Airflow • Kafka • Spark • Cassandra • Docker
- Features: Automated workflows, distributed processing, scalable architecture
- Developed cutting-edge AI models for computer vision applications
- Implemented deep learning solutions for medical image analysis
- Created real-time object detection systems with optimized performance
- Conducted workshops on Machine Learning and Deep Learning
- Mentored students in AI/ML projects and research
- Created educational content for Computer Vision courses
- 🏅 Deep Learning Specialization - Coursera
- 🏅 Machine Learning Engineering - Google Cloud
- 🏅 Computer Vision Nanodegree - Udacity
- 🎖️ Best AI Project Award - University Hackathon 2024
class CurrentFocus:
def __init__(self):
self.learning = ["Agentic AI","MCP Server/Client","GenAI/RAG/LLM","NLP"]
self.learning = ["Advanced Computer Vision", "LLM Fine-tuning", "MLOps/AIOps/LLMOps","Machione Learing"]
self.building = ["AI-Powered Applications", "Real-time Systems"]
self.exploring = ["Generative AI", "Edge AI", "Federated Learning"]
self.open_to = ["Collaborations", "Open Source", "Job Opportunities"]
def get_status(self):
return "🚀 Always learning, always building!"
me = CurrentFocus()
print(me.get_status())- Contribute to 151+ open-source AI projects
- Publish research papers on Computer Vision
- Build and deploy 75+ production-ready AI applications
- Mentor 50+ aspiring AI/ML engineers
- Master advanced LLM architectures and deployment
Thanks for visiting! Let's build something amazing together! 🚀
