Data Engineering · AI Infrastructure The foundation under every engagement.
AI applications, data platforms and agents are only as reliable as the infrastructure underneath them. We engineer that layer first — and we engineer it to last.
- Tier 4
- The base tier of our engagement model — the floor the others stand on
What we engineer
The work your users never see.
Yet it is the first thing they notice when it is missing. We design and operate the infrastructure layer for AI workloads — agentic, generative and traditional ML alike.
Scalable architecture
Horizontal scale by design, not retrofit. Stateless services, queue-isolated workers and regional failover.
Cloud-native design
Containers, Kubernetes and serverless where it fits. Infrastructure as code from day one.
Performance optimisation
Latency budgets per tier, caching strategy, model routing, batched inference and GPU efficiency.
Security hardening
Least-privilege IAM, secrets management, network segmentation, key rotation and vulnerability scanning built into CI/CD.
Stack
Reproducible from day one.
Infrastructure as code
- Terraform
- CDK
- Pulumi
AWS
- Bedrock
- SageMaker
- EKS
- Lambda
- S3
- OpenSearch
On-prem inference
- NVIDIA H100/A100
- vLLM
- TGI
- Ray Serve
- Triton
Cloud-native
- Containers
- Kubernetes
- Serverless
Where we deploy
The deployment target is a constraint, not a creed.
We engineer for where your data and your compliance obligations live.
AWS
- When it earns its place
- Primary cloud. Indian regions for DPDP-aligned workloads; US and EU regions for global ones.
- What we run
- Bedrock, SageMaker, EKS, Lambda, S3, OpenSearch
On-prem GPUs
- When it earns its place
- When sovereignty, latency or cost demands it.
- What we run
- NVIDIA H100/A100 clusters, vLLM and TGI inference servers, Ray Serve and Triton for orchestration
Hybrid
- When it earns its place
- Sensitive workloads stay on-prem and scale-out workloads run in the cloud, both on the same orchestration plane.
- What we run
- VPN/Direct Connect, identity federation and pipeline portability — so the split is operational, not architectural
Edge
- When it earns its place
- When latency is the workload.
- What we run
- Distilled models on edge GPUs, with central coordination for evaluation and updates
The discipline
Engineered to last. Operated that way.
Infrastructure as code
Terraform, CDK and Pulumi. Reproducible from day one.
CI/CD with policy gates
Security scans, cost budgets and performance regressions caught before merge.
Observability on by default
Structured metrics, traces and logs. Measured against SLOs, not gut feel.
Zero-downtime deploys
Blue/green, canary and feature flags. Rollback is built in.
Cost discipline
FinOps tagging at deploy time. Spot instances, reserved capacity and model routing tied to budget.
How it connects
The floor the rest of our engagement model stands on.
The lakehouse and pipelines of the Data Platform run on this layer. The AI agents you direct in the Future of Work execute on it. CI/CD, MLOps and deployment for the GenAI Delivery Factory live here. And we instrument the audit trails, traces and compliance hooks of Responsible AI at this layer.
Next step
Build AI on infrastructure that lasts.
Scalable architecture, cloud-native design, performance optimisation and security hardening — on AWS, on-prem GPUs and multi-cloud where it earns its place.