I design and build scalable software where reliable backend systems, efficient architectures, and data-driven solutions come together. My work spans end-to-end software development, from developing responsive web applications to building secure, high-performance APIs and scalable backend services using modern technologies.
Alongside software engineering, I explore quantitative finance by applying Python, statistics, and data analysis to financial markets, algorithmic trading, risk modeling, and financial analytics. I enjoy transforming complex datasets into meaningful insights through analytical thinking and computational techniques.
I also work with artificial intelligence and machine learning, leveraging Python and modern ML frameworks to build intelligent systems, automate workflows, and solve real-world problems. I am passionate about writing clean, maintainable code, optimizing system performance, and continuously learning emerging technologies to build software that is scalable, reliable, and impactful.
Programming Languages: Python, C++, JavaScript, TypeScript, SQL
Core Computer Science: Data Structures & Algorithms, Object-Oriented Programming, Operating Systems, Database Management Systems, Computer Networks, Software Design Principles
Software Development: REST APIs, System Design Fundamentals, Design Patterns, Concurrent Programming, Performance Optimization, Unit Testing
Frameworks: Node.js, Express.js, FastAPI
Databases: PostgreSQL, MongoDB, MySQL
Data Modeling: Prisma, Mongoose
API Development: RESTful APIs, JWT Authentication, OAuth, API Documentation (Swagger), Postman
Caching & Messaging: Redis (Learning), RabbitMQ (Learning)
Programming: Python
Financial Computing: Time Series Analysis, Statistical Modeling, Portfolio Analytics, Risk Analysis, Factor Modeling, Financial Data Processing
Mathematics: Probability, Statistics, Linear Algebra, Optimization, Numerical Methods
Libraries: NumPy, Pandas, SciPy, Statsmodels
Machine Learning: Supervised Learning, Unsupervised Learning, Feature Engineering, Model Evaluation, Hyperparameter Tuning
Deep Learning: Neural Networks, Transformer Models, Attention Mechanisms
Natural Language Processing: Hugging Face Transformers, Tokenization, Text Classification, Sentiment Analysis
Frameworks: Scikit-learn, PyTorch (Learning), Hugging Face
Data Analysis: Exploratory Data Analysis (EDA), Data Cleaning, Data Validation, Statistical Analysis
Visualization: Matplotlib, Seaborn
Data Processing: Pandas, NumPy
Querying: SQL
Version Control: Git, GitHub
Containerization: Docker
Cloud Platforms: AWS (Learning), Azure (Learning)
Deployment: Vercel, Render, MongoDB Atlas
Operating Systems: Linux
VS Code • Jupyter Notebook • Postman • GitHub Actions • Figma
Portfolio: Sachin Kumar Singh | Portfolio
LinkedIn: Sachin Kumar Singh | LinkedIn
Email: work.sachinks@gmail.com
"Code is valuable when it solves problems. Great software combines engineering, mathematics, and intelligence to create lasting impact."