🚀 MLOps & Backend Engineer
Building production-grade ML platforms and cloud-native backend systems that are scalable, reliable, and observable.
- 4+ years of experience across MLOps, ML platforms, and backend engineering
- Strong focus on end-to-end ML lifecycle — training, deployment, monitoring, retraining
- Experienced in Kubernetes-first, cloud-native architectures
- Enjoy working at the intersection of Machine Learning, Backend Engineering, and Cloud Infrastructure
I like turning complex systems into simple, maintainable, and production-ready solutions.
Languages
Python • SQL • Bash
MLOps & ML Platforms
Kubeflow • Airflow • MLflow • Evidently AI • WhyLabs • Vertex AI • SageMaker
Backend Engineering
FastAPI • Flask • REST APIs • Microservices • OAuth2
Data Engineering
PySpark • BigQuery • Pandas
Infrastructure & DevOps
Kubernetes • Docker • Terraform • GitHub Actions • Jenkins
Cloud
GCP • AWS
- Clean architecture over quick hacks
- Monitoring and observability are first-class citizens
- Reproducibility beats notebook-only workflows
- Systems should fail early, clearly, and safely
- 💼 LinkedIn: https://www.linkedin.com/in/arijitiiest/
- 🌐 Portfolio: https://arijitiiest.github.io
- ✍️ Medium: https://medium.com/@arijit_chowdhury
- 📧 Email: arijitchowdhury8926@gmail.com

