Certifications &
technical learning

  • Google Cloud
  • Redis
  • IBM
  • MongoDB
  • Cisco
  • Jenkins
  • RabbitMQ
  • GraphQL
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Rabat, Morocco

Mustapha
Ibnel

Senior Staff Engineer & Solutions Architect

I advise teams on AI adoption and connect AI capabilities to existing applications, drawing on my background in software architecture, cloud platforms and delivery engineering.

Applications& servicesDelivery& operationsCloudinfrastructure
A view of my engineering scope.Applications & services · Delivery & operations · Cloud infrastructure
Mustapha Ibnel

From leading cloud and delivery engineering at Webloo to co-founding a mobile platform, I work across the boundaries between product, infrastructure and operations.

Selected engineering work

All work

Software architecture, cloud platforms and delivery engineering — the foundation for my AI advisory and integration work.

Cloud & platform engineeringWebloo

Cloud platforms across AWS, Azure and GCP

Leading cloud architecture and implementation across application hosting, infrastructure automation, security and observability.

Software Engineering Lead and DevOps Architect

  • AWS
  • Azure
  • GCP
  • Terraform
  • Ansible
AWSAzureGoogle CloudIdentity · infrastructure as code · observability
Delivery engineering & DevSecOpsWebloo

Delivery systems, from commit to operation

CI/CD architecture that connects application changes, security checks, infrastructure automation and controlled release strategies.

Software Engineering Lead and DevOps Architect

  • Jenkins
  • GitHub Actions
  • GitLab CI
  • Terraform
  • Ansible
CodeBuildChecksReleaseRelease signals inform the next change
Software architecture & application developmentWebloo

Application architecture and real-time systems

Leading full-stack application work across APIs, PostgreSQL, GraphQL, asynchronous processing, notifications and geo-search.

Software Engineering Lead and DevOps Architect

  • React
  • Node.js
  • TypeScript
  • PostgreSQL
  • GraphQL
ClientsAPIs & eventsServicesData & search
Mobile platforms & product engineeringFFAUG LTD

A low-code platform for mobile applications

Co-founding and leading development across Android, Flutter, Firebase integration and the infrastructure supporting a mobile platform.

Co-Founder & Lead Developer

  • Android
  • Kotlin
  • Java
  • Flutter
  • Firebase
Mobile appsIdentityFirebaseBuild & delivery

Connecting existing software to AI.

I help teams decide where AI is useful, how to integrate it, and what needs to change around it.

That starts with the applications, data and workflows already in place. I work through the integration boundaries, safeguards and operating requirements so adoption can happen incrementally.

Discuss an AI integration
Existing systemsIntegrationAI capabilityAccess controls · evaluation · human review
Example integration approach; tool choices depend on the project.

Advisory & adoption

Assess use cases, data readiness and technical constraints. Define a focused pilot with clear evaluation criteria.

Integration & modernisation

Connect existing applications through APIs, retrieval and workflow orchestration. Add AI where it serves the system. For agentic workflows, define tool permissions and human review points.

Guidance for the team

Work through architecture decisions, evaluation practices and operating responsibilities. Build shared understanding through documentation and technical enablement.

Software delivery, from decision to operation

A delivery approach for evolving an existing system: make constraints explicit, validate changes and keep a clear path back when a release fails.

  1. Scope & constraints

    Map the existing system, dependencies, acceptance criteria and operating constraints.

    Brief & acceptance criteria

  2. Architecture & interfaces

    Define API and data contracts, migration boundaries and security requirements.

    Architecture notes & contracts

  3. Build & automate

    Review changes, automate infrastructure and keep builds reproducible.

    Code, tests & build pipeline

  4. Verify & secure

    Run contract, integration and security checks; rehearse rollback.

    Test evidence & release gates

  5. Release & observe

    Stage rollouts with health checks, rollback steps and clear ownership.

    Release plan & operating notes

  6. Feedback & improve

    Feed user feedback, incidents and performance signals into the next iteration.

    Prioritised improvements

CodeBuildChecksReleaseRelease signals inform the next change

Feedback, technical enablement and improvement continue throughout.

Agentic AI Development Lifecycle

From a bounded task to an agent that can use tools, work with existing systems and operate with appropriate human oversight.

  1. Define the task

    Clarify the goal, context, limits and success criteria.

    Task contract

  2. Design agent & tools

    Choose models, define tool permissions and plan review points.

    Tool contracts & review points

  3. Build & integrate

    Implement the agent and connect it to existing applications.

    Agent implementation & integration

  4. Evaluate & review

    Test task outcomes, failure cases and tool-use behavior.

    Evaluation suite & review evidence

  5. Release with controls

    Set access boundaries, monitoring and a recovery path.

    Release & recovery plan

  6. Observe & improve

    Review traces and outcomes; refine prompts, tools and workflows.

    Trace-led improvements

ModelAgent orchestrationTools & APIsContext & stateHuman review

Tool permissions · human approval · traceability · recovery

Feedback, technical enablement and improvement continue throughout.

Engineering scope / An explorable map

The connections
are part of the work.

A feature reaches beyond application code. These are the boundaries I work across, and the responsibilities that connect them.

Conceptual scope, not a client architecture.
Applications

Distributed applications & integration

My application work spans full-stack development, service interfaces and the movement of data between systems.

  • REST and GraphQL interfaces, modular application design
  • Message queues, notifications and real-time application features
  • PostgreSQL, caching and Elasticsearch geo-search

Node.js / TypeScript / React / GraphQL

Explore this work
Delivery

Engineering the path to release

I’ve led delivery automation that connects source changes, security checks, infrastructure and deployment.

  • CI/CD with Jenkins, GitHub Actions and GitLab CI
  • Infrastructure and configuration automation with Terraform and Ansible
  • Blue-green and canary deployment approaches

CI/CD / DevSecOps / Infrastructure as code

Explore this work
Platforms

Cloud architecture with operating context

At Webloo, my scope included cloud architecture and implementation across AWS, Azure and GCP.

  • Application hosting, containers and managed services
  • IAM, encryption and secrets management
  • Platform choices shaped by cost, integration and fault tolerance

AWS / Azure / GCP / Kubernetes / Vault

Explore this work
Operations

Understanding the system after deployment

My background in servers and networks informs how I think about reliability, observability and recovery.

  • Distributed tracing, metrics and alerting
  • Linux, Windows Server, VMware and network operations
  • Documentation and practical handover for operating teams

OpenTelemetry / Prometheus / Grafana / Linux

Explore this work

Open engineering studies

Explore the studies

From the notebook

Writing archive

A notebook taking shape.

I’m preparing notes on software, infrastructure and computing. I’ll publish them here when they’re ready, with code, diagrams and sources where they help.

See the questions I’m exploring
WORKING NOTES
THOUGHTFUL EXPLANATIONS
EXPERIMENTS
Independent work & education

Building
AgentixBrain.

I’m the founder of AgentixBrain. It brings together my work in business software, AI integration and the platforms behind them.

Alongside the engineering work, Academy explores computing and AI through visual lessons and explanations.

Visit AgentixBrain
Currently curious aboutMy now page

AI infrastructure

Model hosting, inference and the systems around AI.

Memory & context

How AI systems can retain and use information over time.

Computing, explained

Mathematics, computing history and technical storytelling.