Platform Engineering
The problem: Your platform buckles under load. Deployments are risky. Teams are blocked by infrastructure they cannot trust.
The outcome: Stable, scalable platforms that teams can ship on confidently. From 20k to 80k req/s.
Proof: Scaled a retail media platform from ~20k req/s to stable 80k req/s using Kafka, KEDA, and event-driven architecture.
What I deliver
- Multi-cloud platform architecture (AWS, Azure, GCP)
- Kubernetes cluster design and operations
- Internal Developer Platforms (IDP)
- Infrastructure-as-Code (Terraform, Pulumi)
- Service mesh and microservices architecture
- Platform migration and modernisation
Cloud & Infrastructure
The problem: Cloud spend is climbing. Nobody knows where the waste is. Architecture decisions from two years ago are costing you today.
The outcome: Clear visibility into waste. Actionable cost reductions. Architecture that scales without surprise bills.
Proof: Removed £50K/month in observability costs by fixing high-cardinality metrics, over-provisioned infrastructure, and inefficient queries.
What I deliver
- Cloud cost optimisation and FinOps
- Multi-account and landing zone setup
- Security hardening and compliance
- Disaster recovery and high availability
- Reserved Instance and Savings Plan strategy
- Cloud-native architecture design
SRE & Observability
The problem: Dashboards look green. Customers are still down. Your alerting catches symptoms, not causes. One bad deploy from a full outage.
The outcome: Observability that surfaces real problems. Teams that respond in minutes, not hours.
Proof: Found 28 issues across a LGTM stack (Mimir, Loki, Tempo) before they caused production outages. Single-replica components, misconfigured scaling, hidden failure points.
What I deliver
- OpenTelemetry instrumentation
- Prometheus, Grafana, and Splunk deployments
- Distributed tracing and log correlation
- SLO/SLI definition and alerting strategy
- Incident response and runbook automation
- Observability platform architecture
AI Systems Integration
The problem: AI works in notebooks. It fails in production. Your platform is not ready for the reliability, latency, and data quality that AI demands.
The outcome: AI that runs reliably in production. Not demos. Not prototypes. Real systems that work at scale.
Proof: Built an LLM-based ticket classification pipeline that took accuracy from 59% to 96%. Slack to backend to LLM to database, with crash recovery and prompt versioning.
What I deliver
- LLM API integration and orchestration
- RAG (Retrieval-Augmented Generation) systems
- AI pipeline infrastructure
- Vector database setup and management
- Model serving and scaling
- AI-ready platform assessment
Data Engineering
The problem: Pipelines take hours. Data is stale. Teams make decisions on yesterday's numbers because the infrastructure cannot keep up.
The outcome: Fast, reliable data pipelines. Fresh data. Decisions based on what is happening now.
Proof: Cut analytics pipeline runtime from ~1 hour to 20 minutes through ETL redesign, query optimisation, and event-driven architecture.
What I deliver
- ETL/ELT pipeline design and optimisation
- Event-driven architecture (Kafka, Kinesis)
- Snowflake platform engineering
- Data lake and warehouse architecture
- Real-time streaming and processing
- Data quality and governance frameworks
DevOps & Automation
The problem: Every release is a risk. Deploys cause incidents. Manual processes slow the team down and introduce errors.
The outcome: Confident releases. Automated pipelines. Teams that ship daily without breaking production.
Proof: Automated compliance pipeline using AWS CodePipeline, deterministic finding IDs, and Security Hub integration. Zero duplicate findings. Fully automated lifecycle.
What I deliver
- CI/CD pipeline design (GitHub Actions, GitLab, Jenkins)
- GitOps workflows (ArgoCD, Flux)
- Deployment strategies (blue-green, canary, rolling)
- Infrastructure automation and self-service
- Developer experience improvements
- Environment management and promotion