ITS MART Hiring! Full Time Back End Developer in Federal Territory - Ricebowl

You will be the senior engineer on a small product team building a secure serialisation and verification platform — a system that issues uniquely identified, cryptographically protected printed codes at volume, and verifies them again in the field.

The role spans backend services, ERP integration, cloud and on-premises infrastructure, and machine learning. It suits an engineer who prefers owning a whole system to owning one layer of it. There are no legacy codebase and no platform team ahead of you; the conventions you set early are the ones the team will keep.

What you will do:

• Code generation and issuance
• Verification and records
• Platform, cloud and integration
• Machine learning

Requirements:

• Six or more years of professional software engineering, including owning a production system end to end — you designed it, deployed it and were on call for it.
• Go — at least eighteen months of real production work. Strong engineers from Java, Kotlin, C# or Rust who have deliberately moved to Go will be considered.
• Python — confident enough to work inside a machine learning codebase, not only to write scripts.
• Google Cloud — production services on GCP (GKE or Compute Engine, Cloud Storage, Cloud KMS) with infrastructure as code (Terraform) and containers (Docker). Deploying the same workload into an on-premises data centre is a significant advantage.
• Key management and applied security — you have used a KMS or HSM properly. You know why secrets do not belong in environment variables and why a cryptographic random number generator is not interchangeable with an ordinary one.
• PostgreSQL — beyond basic queries. You have made indexing decisions, handled transaction isolation and survived a migration that went wrong.
• Odoo — practical experience integrating with or extending Odoo. You have used its XML-RPC or JSON-RPC API and have built or modified at least one custom module. Deep Odoo functional consulting is not required.
• Machine learning in practice — you have trained and evaluated models with PyTorch or TensorFlow, and can explain how you measured whether a model was genuinely working rather than merely scoring well.