I design cloud platforms and agentic AI systems teams can trust.
I combine principal-level AWS and Azure architecture experience with hands-on work in AI agents, controlled tools, evaluation, human oversight, and the platforms that make those systems reliable.
Open to senior and lead roles · Available for focused consulting
Selected work
Explore agentic applications, governed platforms, and cloud systems alongside professional architecture work. Each case explains what the system does, where authority sits, and what was actually verified.
Agentic systems and their engineering foundations
Working applications and platforms with reviewable architecture, behavioral evidence, controlled authority, and explicit deployment boundaries.
Secure Self-Service Cloud Platform
v1.0.0 · stable · simulation onlyDevelopers submit through a portal, API or CLI; policy evaluates the request and produces deterministic evidence for human review. The public release demonstrates the simulation, proposal generation, Terraform validation and protected workflow design. It does not call a cloud API, create resources, or validate a real environment.
- Readable policy feedback across ownership, cost, network, encryption, logging and expiry.
- Deterministic evidence with request IDs, fingerprints, content hashes and expected resources.
- Separate provider roots for AWS and Azure, with protected workflow designs and short-lived OIDC boundaries.
- Credential-free local path covering the portal, API, CLI, proposal generation and validation.
Legacy Application Modernization
Waves 0–3 · local + hosted CI verified · AWS target unappliedA deliberately constrained order service modernized without losing its HTTP contract. The project separates locally verified application behavior from a reviewable AWS target, using a non-root container, operational runbooks, and plan-only Terraform rather than implying a deployment.
- Preserved /v1 behavior with validation, idempotency, ownership, transitions, reports and safe error envelopes.
- Runtime hardening through non-root, read-only container behavior, health/readiness boundaries and immutable image input.
- Operational seams for PostgreSQL, JWT, SQS, S3, telemetry, graceful shutdown and recovery runbooks.
- Deterministic evidence covering tests, container acceptance, Trivy, SBOM, PostgreSQL integration and backup/restore.
Secure URL Shortener Platform
v0.1.0 · local + CI verified · AWS target unappliedA local-first URL shortening service with explicit ownership, lifecycle and resolver boundaries. The public v0.1.0 release verifies the local Compose/PostgreSQL lifecycle, Terraform validation, pinned multi-platform images and CI evidence; it has no public endpoint and no applied AWS environment.
- Secure lifecycle with custom/random aliases, expiry, disable, tombstone deletion and no code reuse.
- Destination safety through HTTP(S)-only validation, DNS checks and rejection of private or metadata addresses.
- Bounded resolver with safe click metadata, no client IP/user-agent storage and explicit 302/404/410 semantics.
- Reviewable delivery design with Terraform validation, CI quality/security gates and no AWS apply or endpoint claim.
Secure Container Delivery & GitOps
GitOps v0.1.0 · local Docker Desktop verified · image v0.1.3 publishedTwo repositories make the ownership boundary visible: the application repository tests, scans and publishes an immutable multi-architecture image; the GitOps repository owns reviewed Kubernetes desired state and Argo CD reconciliation to a Docker Desktop cluster. This is verified locally, not a managed-cloud or production deployment.
- Application repository owns tests, non-root image build, vulnerability scanning and multi-architecture GHCR publication.
- GitOps repository owns reviewed manifests, Kustomize overlays, probes, security context, NetworkPolicy and Argo CD reconciliation.
- Immutable hand-off uses the published digest rather than a moving tag.
- Operational evidence includes a diagnosed broken image, Git revert recovery, rollout checks and endpoint verification on Docker Desktop.
Application repository → · GitOps repository → · Read architecture docs →
AWS Customer Support Assistant
v1.0.0 · local + CI + bounded AWS lifecycle verifiedA focused generative-AI support workflow: customers receive policy-grounded answers, review a proposed escalation, and explicitly confirm before a ticket is created. An authenticated operator desk then advances tickets through resolution. The serverless AWS deployment was verified and removed after acceptance; no continuously hosted endpoint is claimed.
- Grounded assistance combines Bedrock Converse, Knowledge Bases, citations and Guardrails with deterministic local safety checks.
- Controlled escalation requires explicit confirmation and idempotency before ticket creation.
- Authenticated operations uses Cognito PKCE and operator-group authorization for ticket progression.
- Cost-aware lifecycle includes expiry, throttles, short retention, alarms, manual OIDC deployment and exact-target teardown.
View repository → · View v1.0.0 release → · Read AWS verification →
CircuitCare AI Support
complete · local + CI + bounded AWS lifecycle verifiedAn agentic support system for a fictional electronics retailer. The assistant combines grounded knowledge, cross-session memory, approved Gateway tools, restricted browsing, and isolated calculations. Customers can view orders, confirm eligible returns, and follow safety cases while trusted application rules retain authority over identity, ownership, eligibility, and every business change.
- Identity-owned workflows prevent one customer from reading or changing another customer's records.
- Deterministic authority keeps return eligibility, confirmation, idempotency and audit decisions outside the language model.
- Bounded AgentCore capabilities provide approved knowledge, memory, Gateway tools, restricted research and isolated calculations without general business-record access.
- Evaluation and acceptance evidence combines 30 versioned scenarios with automated invariants, live adversarial checks, deployment, teardown and residual-resource checks.
View repository → · Read validation evidence → · Read security model →
Related work: Secure Document Intelligence applies cited extraction, hostile-input handling, confidence thresholds, and human review to document processing. It demonstrates controlled AI automation rather than an autonomous agent.
Architecture in production contexts
Detailed engagements showing the systems, operating models, and outcomes behind the work.
Project Hafnia — Visual AI & Data Insights Platform
Milestone Systems × NVIDIACloud architecture lead for a Milestone × NVIDIA collaboration enabling visual AI model development on annotated, regulation-compliant video data. Owned from concept through production deployment as the primary architecture authority.
- Enterprise AI/data platform on AWS and Azure for building, training and operationalising visual AI models.
- Multi-account AWS foundation — Organizations, Control Tower, centralised logging and cost governance.
- Secure hybrid integration between on-premises infrastructure and AWS for reliable, controlled data movement.
- ML enablement via SageMaker and Kinesis Video Streams, plus large-scale ingestion, annotation and training pipelines.
- AWS partnership initiative contribution involving a forthcoming AWS service planned for public release.
Distribution Centers Hub Platform
LEGOCloud infrastructure and automation for LEGO's distribution centre platform — Infrastructure as Code, CI/CD and Kubernetes operations, with the emphasis on deployment reliability. In a logistics context a failed release is not an inconvenience; it is pallets not moving.
- Terraform-managed AWS infrastructure enabling repeatable, reliable environment provisioning.
- CI/CD with GitHub Actions and Argo CD, cutting manual deployment effort substantially.
- Containerised workloads on Amazon EKS, packaged and configured with Helm.
- IAM hardening plus Sysdig and PagerDuty for monitoring and incident response.
Cloud Modernisation, Migration & Operations
AgillicA full reshaping of the cloud operating model for a production SaaS business — migration, automation, monitoring, security and disaster recovery. The headline saving came from architecture redesign rather than tactical right-sizing, with performance improving at the same time.
- 85% reduction in operational cost through AWS architecture redesign, with performance and scalability gains.
- AWS migrations for business-critical systems including IBM SPSS Modeler and content delivery workloads.
- Multi-account governance with Organizations and Control Tower for compliance and operational control.
- Disaster recovery patterns and automated CI/CD delivered via Terraform and GitLab CI.
What I'm brought in to do
I work where agentic behavior meets the controls of a real platform: identity, data, tools, evaluation, security, deployment, observability, and accountable human decisions.
Agentic Applications
AI assistants and agents that combine grounded knowledge, memory, and bounded tools with clear authority limits and useful customer or staff workflows.
Agent Evaluation & Human Oversight
Versioned scenarios, deterministic invariants, adversarial checks, citations, confirmation steps, and human review for decisions that require judgment.
Controlled Tools & Business Authority
Narrow tool contracts, identity-aware access, idempotent changes, audit trails, and application rules that remain authoritative when model output is uncertain.
Coding-Agent Workflows
Bounded task definition, context management, implementation review, integration, and evidence-led testing while retaining engineering accountability for the result.
Cloud Architecture & Governance
AWS and Azure target architecture, landing zones, multi-account strategy, identity, networking, security, cost governance, and reviewable architecture decisions.
AI & Data Platforms
Managed AI services, governed knowledge, large-scale ingestion, annotation workflows, data governance, and ML training platforms on AWS.
Platform Engineering & Modernization
Self-service workflows, infrastructure as code, CI/CD, containers, Kubernetes, GitOps, and migration paths that turn architecture into repeatable delivery.
Operational Reliability & Cost
Observability, security, failure isolation, incident response, recovery, and architecture-level cost control for cloud and AI workloads.
Cloud architecture depth, applied to agentic systems.
- Clear decisionsMake trade-offs explicit and reviewable.
- Operable systemsDesign for the team after handover.
- Accountable deliveryUse speed without outsourcing judgment.
At a glance
- Based
- Copenhagen · London
- Focus
- Cloud architecture · agentic AI · platform engineering
- Availability
- Senior / lead roles & consulting
- Work authorisation
- DK & UK — no sponsorship needed
- Education
- MSc Cloud Computing
- Languages
- Arabic (native) · English (fluent) · Danish (beginner)
Career timeline
Fifteen years from infrastructure engineering to principal-level cloud architecture, now forming the operational foundation for my work in agentic AI systems.
Cloud strategy, solution design, and secure platform delivery across AWS and Azure, with current work extending those disciplines into agentic AI applications, evaluation, controlled tools, and reliable AI platforms.
Architecture authority for Project Hafnia, the Milestone × NVIDIA visual AI data platform, taking the collaboration from concept through production deployment.
Senior cloud architect supporting the early architecture, design and implementation of Project Hafnia for client Milestone Systems. The dates overlap with the role above because I continued on the same project after moving from consultancy to Milestone directly.
Infrastructure as Code, CI/CD and Kubernetes operations for the Distribution Centers Hub platform, with deployment reliability as the central outcome.
Led AWS modernisation, migration, governance and disaster-recovery work across production SaaS environments, delivering an 85% operational cost reduction.
Built the operating foundation across Windows Server and Linux administration, Active Directory, SharePoint, DNS, infrastructure, backup and disaster recovery, while teaching IT and computing along the way.
Verified across both major clouds
Fourteen AWS and Microsoft certifications. Three sit at professional or expert level — the tier that requires demonstrating architecture judgement rather than service knowledge.
The senior AWS architecture credential — multi-tier, multi-account design under real cost and compliance constraints.
Provisioning, operating and managing distributed systems, with CI/CD and automated governance at its core.
The equivalent expert-level Azure credential, covering identity, networking, storage and compute strategy.
Verify every badge on Credly →
Technical toolkit
Agentic AI and cloud architecture share the same engineering foundation: clear interfaces, controlled access, repeatable delivery, evidence, and reliable operations.
Cloud & Architecture
Agentic AI & Data
Delivery & Platform
IaC & Automation
Security & Governance
Observability & Operations
Let's talk cloud and agentic AI
Open to senior and lead roles spanning cloud architecture, agentic AI, and platform engineering in Denmark and the United Kingdom, and to focused consulting engagements. No sponsorship required.