Cloud-Native Backend & MLOps in India
High-performance backends and the MLOps to deploy, monitor, and retrain models in production — for SaaS, AI products, and real-time data platforms. Delivered to India teams on your calendar.
High-performance backends and the MLOps to deploy, monitor, and retrain models in production — for SaaS, AI products, and real-time data platforms. Indian businesses operate at scale where automation and robust platforms compound fast. We engineer high-volume systems built to be maintained and extended.
Software built for Western transaction volumes buckles under India's scale, and a card-first payment flow is already the wrong default when UPI is the primary rail. The DPDP Act adds a data-protection layer that vendors either ignore or discover mid-build, instead of scoping it before the first sprint. That is the environment cloud-native backend & mlops has to be built into, not around.
- 01Architect services and the ML lifecycle around real load and SLAs.
- 02Prototype deployment and monitoring before scaling.
- 03Forge microservices, pipelines, and IaC.
- 04Harden with A/B deployment, drift monitoring, and retraining.
- Microservices (Go, Rust, Node.js, .NET)
- Serverless & container orchestration (K8s, Nomad)
- CI/CD (GitHub Actions, GitLab CI, ArgoCD)
- MLOps: deployment, A/B, monitoring, drift
- Data engineering (feature stores, ETL)
- Infrastructure as Code (Terraform, Pulumi)
- Real-time event streaming
- Event-driven architecture
- UPI-native payment flows, not a card-first architecture retrofitted
- High-throughput architecture built for national-scale transaction volume
- DPDP Act-aware data handling, scoped at Blueprint
- IST-aligned delivery with real-time overlap
- Cost-efficient infrastructure architecture for margin-sensitive scale
What you can expect
Questions, answered straight
Yes. Aligned to IST working hours. Software and AI built for India's operational scale — high-volume automation and platforms that hold up under real load.