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Germain Safari

• Apr 2

Engineered and maintained Azure cloud environments supporting DevOps, identity, and machine learning workloads. Built and managed CI/CD pipelines, automated deployments, configured infrastructure and access policies through Microsoft Entra ID, and supported ML lifecycle workflows using Azure ML and Azure AI Foundry. Helped deliver secure, scalable, and production-ready cloud solutions for application and AI services.

Cloud Infrastructure Cloud Security Azure

Ehtasham Ali

pro

• Sep 28

What if anyone on your team could ask your data a question and get a live dashboard back in under a minute?

That was the brief. Business users were locked out of their own data. Every question waited on someone who could write SQL, data sat across disconnected systems, and security teams refused to send sensitive records to third-party AI tools.

So we flipped the model. Instead of moving enterprise data to an AI product, we moved the AI analytics product into the customer's AWS account.

What we built: • Natural-language querying that turns plain-English questions into governed dashboards in under 60 seconds • Federated queries across SQL and NoSQL sources through Trino • AI query generation on AWS Bedrock Agents, with Bedrock Guardrails keeping model output in bounds • Dashboards generated with Apache Superset, plus proactive anomaly and trend alerts • A white-label React widget that embeds in any product • Role-based access, row-level security and enterprise SSO enforced at every layer

The result: Zero data egress. The whole platform deploys inside the customer's VPC and is live on AWS Marketplace.

Building AI features for a data-heavy or regulated product? Let's talk about doing it without your data leaving your cloud.

b2b saas AI Development

TRUEFRAME

max

• Sep 29

zero data egress with in-cloud deployment is HUGE ehtasham. how quick is the federated query response?

Maxmus studio

• Sep 28

Building AI for healthcare leaves zero room for error.

I’m currently collaborating with an incredible team on Raphald AI, a medical detection application. Building the systems for a project with stakes this high is a massive reminder that the underlying backend architecture matters just as much as the machine learning model itself.

When integrating diagnostic AI, your API endpoints cannot drop requests, and your database workflows demand absolute integrity. You aren't just passing JSON payloads; you are handling critical, real-time workflows where stability is non-negotiable.

Engineering these systems continues to shape my approach to building robust Python backends. If you are developing a product that requires reliable AI integration or rock-solid FastAPI infrastructure, check out the newly updated services on my profile. Let's build something that works when it counts.