Cinematic portfolio for a production-focused engineer — deliberate systems, measurable impact.
A Full-Stack Engineer & AI/ML practitioner based in India. I build production-grade web platforms, data pipelines, and machine learning systems that are observable, testable, and designed for real users.
- Title: Full-Stack Developer & AI/ML Engineer
- Location: India
- Open to: Collaborations · Open Source · Roles & Internships
Build reliable, measurable software that answers product questions — shipped with quality, operated with clarity.
┌──────────────────────── ENGINEERING MANIFESTO ───────────────────────┐
│ Product-first: features must solve user problems, not decorate UI. │
│ Observable: metrics and logs are part of the product. │
│ Testable: automated tests that guard behaviour and contracts. │
│ Incremental: iterate quickly, but ensure each step can be operated. │
│ Maintainable: favor clarity and conventions over cleverness. │
└─────────────────────────────────────────────────────────────────────┘
- End-to-end web platforms: Next.js frontends + Node/Express backends, production deployments on Vercel and cloud providers.
- Machine learning systems: reproducible pipelines, model training, evaluation, and serving with retraining cycles.
- Developer tooling: automation, CI/CD, reproducible dev environments, and debugging workflows.
A compact, memorable identity that guides decisions.
┌─────────────── SIGNAL PANEL ───────────────┐
│ Handle : @Sayam09das │
│ Focus : Scalable APIs · ML Pipelines │
│ Stack : Next.js · Node · PyTorch │
│ Style : Product-minded, test-first, fast │
│ Goal : Ship production systems with care │
└─────────────────────────────────────────────┘
I present tools grouped by role rather than a laundry list — this is how I build and operate systems.
Frontend
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Backend
|
Datastores & Cache
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AI / ML
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Cloud & DevOps
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Tools & Languages
|
I highlight measurable impact, role, and current status for each project.
| Project | What & Why | Tech | Status | Links |
|---|---|---|---|---|
| AI-Powered Anomaly Detection | A pipeline that detects anomalies using ensemble and deep-learning approaches. Focused on precision, explainability, and low-latency batch evaluation for time-series operational data. | Python · PyTorch · Scikit-learn | Production-ready | Repo · Demo |
| LiteSQL | Minimal database engine that demonstrates query parsing, indexing, and storage design for educational use and systems experiments. | C++ · Python | Research / Library | Repo |
| Feature Selection Engine | Benchmark suite for feature selection algorithms, supporting reproducible experiments and comparison across performance and runtime metrics. | Python · Scikit-learn | Active | Repo |
| FinOps Suite | Full-stack finance dashboard with analytics, auth, and scaling considerations — used as a sandbox for integrations and UX-first data presentation. | Next.js · TypeScript · PostgreSQL | Deployed | Repo · Live |
- Building: Production-ready full-stack & ML pipelines (Next.js + Node + PyTorch).
- Learning: System design for reliability and distributed model serving.
- Reading: Papers on model interpretability and observability.
- Open Source: Reproducibility tooling for ML experiments.
| 2024 Polish ML libraries · Ship FinOps features |
2025 Scale ML infra · Productionize monitoring |
2026 Cloud-native architectures · Open source releases |
2027+ Lead engineering teams · Publish ML tooling |
- Delivered multiple full-stack applications with CI, monitoring, and automated deployments.
- Built ML pipelines with reproducible experiments and clear evaluation metrics.
- Strong practice in systems thinking, performance tuning, and DSA (C++/Java).
Extended Achievements (click to expand)
- Competitive programming practice in C++ & Java.
- Hackathon submissions and prototype builds.
- Certifications and courses in ML and backend engineering (add specifics here).
$ whoami > Sayam Das — Full-Stack Engineer & AI/ML $ mission > Build measurable software. Operate with clarity. $ coffee > ☕ 1 cup — commit to meaningful work.
I prefer small, high-quality PRs that add tests, documentation, and operational clarity. My contributions emphasize reproducibility and maintainability.
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