🚀 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: [email protected]