🎯 Production-proven ML Engineer with 9 years building scalable infrastructure and backend systems for AI-driven applications. Expert in cloud infrastructure management (AWS), containerization, infrastructure as code (Terraform), and backend development using modern frameworks.
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🔍 Click to expand key achievements
- Architected production infrastructure using Terraform for reproducible, secure deployments
- Built cloud infrastructure on AWS (EC2, ECS, RDS, S3, CloudWatch, Lambda, SageMaker)
- Infrastructure as code practices reducing deployment complexity
- Developed high-performance backend services using Node.js and Python FastAPI
- Built microservices architecture handling 3x traffic spikes with auto-scaling
- Optimized database performance across MongoDB and PostgreSQL systems
- Containerized services with Docker and orchestrated with Kubernetes
- Implemented CI/CD pipelines using GitHub Actions with comprehensive testing
- 85% reduction in deployment failures through automation
- Collaborative filtering recommender system with measurable engagement improvements
- Real-time emotion detection system achieving high accuracy in production
- End-to-end ML pipeline from design to deployment
- Database optimization: Enhanced data retrieval speed by 32%
- Automated data pipelines: Built SQL/NoSQL integration with monitoring
- AI-driven decision tools: Created strategic data solutions
- Order acceptance optimization: Developed mediator-based automation system
- Genetic algorithms: Applied ML optimization in MATLAB for decision-making
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Indian Institute of Technology (IIT), Kanpur
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CSVTU, Bhilai
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🎓 Stanford University - Algorithms Specialization ✅ 🔗 View Certification
- Divide and Conquer, Sorting and Searching, and Randomized Algorithms
- Graph Search, Shortest Paths, and Data Structures
- Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
- Shortest Paths Revisited, NP-Complete Problems and What To Do About Them
- Advanced algorithm design and analysis techniques
- Time and space complexity optimization
- Graph algorithms and dynamic programming mastery
- NP-completeness theory and approximation algorithms
- Randomized algorithms and probabilistic analysis
- Scalable recommendation algorithm optimization
- High-performance data processing systems
- Performance-critical ML infrastructure implementations
- Algorithmic trading and financial modeling systems
🤖 DeepLearning.ai - Deep Learning Specialization ✅ 🔗 View Certification
- Neural Networks and Deep Learning - Foundational concepts and implementation
- Improving Deep Neural Networks - Hyperparameter tuning, regularization, optimization
- Structuring Machine Learning Projects - Best practices and project management
- Convolutional Neural Networks - Computer vision and image processing
- Sequence Models - RNNs, LSTMs, attention mechanisms, and transformers
- Deep neural network architecture design and implementation
- Advanced CNN techniques for computer vision applications
- RNN/LSTM/GRU for sequence modeling and time series analysis
- Hyperparameter optimization and advanced regularization techniques
- ML project structuring, diagnosis, and performance improvement strategies
- Transfer learning and multi-task learning approaches
- Medical image analysis and diagnostic systems
- Real-time computer vision and object detection
- Natural language processing and sentiment analysis
- Time series forecasting and sequential data modeling
🤖 DeepLearning.ai - TensorFlow Developer Specialization ✅ 🔗 View Certification
- Introduction to TensorFlow for AI, ML, and Deep Learning - Core TensorFlow fundamentals
- Convolutional Neural Networks in TensorFlow - Advanced computer vision techniques
- Natural Language Processing in TensorFlow - Text processing and NLP models
- Sequences, Time Series and Prediction - RNNs, LSTMs, and forecasting models
- TensorFlow 2.x ecosystem mastery and production deployment
- Computer vision with TensorFlow including transfer learning
- Natural language processing with embeddings and sequence models
- Time series analysis and forecasting with RNNs and CNNs
- Model optimization and TensorFlow Serving deployment
- Real-time inference and mobile deployment with TensorFlow Lite
- Production-scale ML model deployment and serving
- Real-time image classification and object detection systems
- Text analysis and sentiment classification applications
- Time series forecasting for business analytics and IoT
🤖 DeepLearning.ai - Generative Adversarial Networks (GANs) Specialization ✅ 🔗 View Certification
- Build Basic Generative Adversarial Networks (GANs) - Foundational GAN concepts and implementations
- Build Better Generative Adversarial Networks (GANs) - Advanced techniques and StyleGAN
- Apply Generative Adversarial Networks (GANs) - Real-world applications and image-to-image translation
- GAN architecture design from basic to advanced implementations
- Advanced GAN variants including DCGAN, WGAN, StyleGAN, and Pix2Pix
- GAN evaluation using Fréchet Inception Distance (FID) and bias detection
- Image-to-image translation and conditional generation techniques
- Understanding of social implications, bias detection, and privacy preservation
- PyTorch implementation for custom GAN architectures
- Synthetic data generation for privacy-preserving machine learning
- Data augmentation for improving model robustness and performance
- Creative applications in art, design, and content generation
- Image-to-image translation for satellite imagery and mapping applications
🤖 DeepLearning.ai - Machine Learning Engineering for Production (MLOps) ✅ 🔗 View Certification
- Introduction to Machine Learning in Production - ML system design and deployment concepts
- Machine Learning Data Lifecycle in Production - Data validation, versioning, and lineage
- Machine Learning Modeling Pipelines in Production - Model development and automation
- Deploying Machine Learning Models in Production - Scalable deployment and monitoring
- End-to-end ML system design and production architecture
- Data lifecycle management with TensorFlow Extended (TFX)
- Model versioning, experiment tracking, and A/B testing frameworks
- Production deployment strategies including canary releases and blue-green deployments
- ML monitoring, model drift detection, and automated retraining
- Fairness, explainability, and responsible AI practices in production
- Enterprise-scale MLOps infrastructure and CI/CD pipelines
- Automated model deployment and monitoring systems
- Production model performance tracking and optimization
- Scalable ML platform architecture for multiple teams and models
🔵 IBM - AI Foundations for Business Specialization ✅ 🔗 View Certification
- Introduction to Artificial Intelligence (AI) - Business-oriented AI fundamentals
- What is Data Science? - Data science concepts and business applications
- The AI Ladder: A Framework for Deploying AI in your Enterprise - Strategic AI implementation
- AI strategy development and business case creation
- Understanding of AI technologies and their business applications
- Data science methodology and its role in AI initiatives
- AI Ladder framework for enterprise AI deployment
- AI ethics, responsible AI practices, and risk assessment
- ROI analysis and business value measurement for AI projects
- Strategic AI transformation planning for enterprises
- Executive stakeholder communication and AI education
- Business case development for AI initiatives and digital transformation
- AI governance and responsible AI implementation strategies
🔵 IBM - Introduction to Data Science Specialization ✅ 🔗 View Certification
- What is Data Science? - Fundamentals and career overview
- Tools for Data Science - Jupyter, RStudio, GitHub, and data science ecosystems
- Data Science Methodology - CRISP-DM and systematic problem-solving approaches
- Python for Data Science, AI & Development - Core Python programming for data analysis
- Python Project for Data Science - Hands-on project with real datasets
- Databases and SQL for Data Science with Python - Database management and SQL queries
- Data science methodology and project lifecycle management
- Python ecosystem mastery including pandas, numpy, and matplotlib
- SQL proficiency for data extraction and database management
- Data visualization and statistical analysis techniques
- Jupyter Notebook development and version control with GitHub
- End-to-end data science project execution and presentation skills
- Data pipeline architecture and ETL process development
- Database optimization and complex query performance tuning
- Statistical analysis and data-driven business intelligence
- Data visualization dashboards and reporting systems
🔵 IBM - Key Technologies for Business Specialization ✅ 🔗 View Certification
- Introduction to Cloud Computing - Cloud fundamentals, service models, deployment models
- Introduction to Artificial Intelligence (AI) - AI concepts and business applications
- What is Data Science? - Data science foundations and industry applications
- Cloud computing fundamentals including IaaS, PaaS, and SaaS models
- Understanding of public, private, and hybrid cloud deployment strategies
- AI and machine learning concepts for business applications
- Data science methodology and its role in modern enterprises
- Cloud-native technologies including microservices and DevOps practices
- Emerging technologies like serverless computing and application modernization
- Enterprise cloud migration and infrastructure modernization
- AI strategy development and implementation planning
- Data-driven business transformation initiatives
- Cloud-native application architecture and development
💡 Innovation: Real-time document processing at scale
🛠️ Tech Stack: Docker, Advanced NLP (NER, Sentiment Analysis), Apache Spark
📈 Impact: 10x faster processing than legacy systems
🎯 Scale: Millions of documents processed daily
💡 Innovation: Hybrid ML approach (Autoencoders + LSTM + Isolation Forest)
🛠️ Tech Stack: TensorFlow, PyTorch, Time-Series Analysis
📈 Impact: 95% reduction in false positives
🎯 Application: Financial fraud detection & system monitoring
💡 Innovation: U-Net architecture for precision medical analysis
🛠️ Tech Stack: Computer Vision, Deep Learning, Medical Imaging
📈 Impact: Sub-pixel accuracy for critical diagnostics
🎯 Application: Assists medical professionals in diagnosis
💡 Innovation: Computer vision + sensor fusion integration
🛠️ Tech Stack: OpenCV, Deep Learning, IoT Sensors
📈 Impact: Enhanced navigation and safety systems
🎯 Application: Autonomous vehicle decision-making
| Performance Area | Achievement | Business Impact | Context |
|---|---|---|---|
| System Reliability | 99.5% uptime | Zero critical downtime incidents | Multi-service platform (5 years) |
| Infrastructure Efficiency | 35% faster deployments | Reduced release cycles from 2 weeks to 3 days | Team of 8 engineers |
| Team Productivity | 40% development velocity boost | Delivered 15+ features per quarter | Custom tooling & automation |
| Operational Excellence | 70% reduction in deployment issues | Saved 20+ hours/week of manual intervention | CI/CD pipeline optimization |
| Data Performance | 32% faster query response | Improved user experience metrics | Database optimization project |
| ML Model Performance | 92% production accuracy | Reduced customer support tickets by 25% | Real-time emotion detection system |
| Cost Optimization | 28% infrastructure cost reduction | $120K annual savings | AWS resource optimization |
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5+ Years ML Infrastructure Lead |
Research Excellence Operations Research |
Global Experience Research Assistant |
Cloud Expertise Enterprise Solutions |
🔥 Currently Powering Production Systems:
- Infrastructure: 15+ AWS services, Kubernetes clusters, Terraform modules
- Backend: Node.js microservices, Python APIs, PostgreSQL & MongoDB
- ML Pipeline: TensorFlow serving, PyTorch models, Apache Spark processing
- Monitoring: Prometheus metrics, Grafana dashboards, CloudWatch alerts
🎯 Senior ML Engineer | 🏗️ ML Infrastructure Architect | 👨💼 Technical Leadership | 🚀 AI Strategy Consulting | 🔬 Research Partnerships
Ready to deliver immediate impact: ✅ Remote-first ✅ Global relocation ✅ Contract or full-time ✅ Immediate availability
"From IIT research labs to production systems serving millions — I architect AI solutions that scale"