Programs of work led by Mihir Sheth across cloud platforms, software delivery, quality engineering, AI in the software lifecycle, endpoint management and security operations.
Several disciplines, one accountability
Lead software development, quality engineering and CloudOps as a single function, owning technology strategy, delivery roadmaps, technical debt and the operational risk that comes with running the platform.
Compliance as engineering work, not paperwork
Contributed to achieving both SOC 2 and ISO 27001 certification, with a focus on risk management, and authored the IT policies and procedures that turned the controls into how the business actually operates.
Where manual operations stop being viable
Eliminated manual database operations across more than 300,000 Azure SQL databases using Azure Functions, and built the archival, replication and failover automation around them.
Policy with an enforcement point behind it
Established device management on Microsoft Intune and authored the MDM and Group Policy standards behind it, so endpoint compliance is enforced and evidenced rather than assumed.
Automated coverage as the thing that sets the pace
Built automated testing frameworks on Jenkins and Test Studio that cut release cycles by 50% while holding the quality bar, having first taken the organisation from ad hoc testing to over 55% automated coverage.
A platform you can rebuild from a repository
Moved cloud infrastructure into version-controlled code with ARM templates, Terraform, Python and PowerShell, automated deployment on top of it, and added the monitoring and SLA reporting that makes the platform legible.
Improving scalability without stopping delivery
Migrated applications onto .NET Core and Blazor Server to improve scalability and performance, sequenced so feature delivery continued throughout.
Built in-house, not switched on from a vendor
Designed and built the AI capability that runs across our software development lifecycle. Not a licence we activated: an architecture I designed, with the scoping, guardrails and enablement that decide whether AI in engineering is leverage or liability.