Service
Cloud Platforms for AI
AWS platforms that power production AI—from infrastructure and CI/CD to observability, security, and scalable cloud architecture.
The problem
AI applications often begin with a model API, a small web service, and enough cloud infrastructure to make the first version available.
That can be sufficient for a prototype. It is rarely sufficient for a product that has real users, private data, external integrations, changing workloads, and expectations around availability and security.
As the application grows, infrastructure decisions begin to affect product delivery. Deployments become risky, environments drift apart, failures are difficult to diagnose, and security controls are added inconsistently across services.
AI introduces additional pressure. Workloads can be bursty, inference costs are variable, data moves across multiple services, and teams need visibility into both conventional application behaviour and model-driven operations.
Common patterns:
- A prototype runs in one environment with no repeatable path to production
- Infrastructure changes depend on manual steps and individual knowledge
- Application, model, retrieval, and data services are deployed without clear boundaries
- Teams can see that a request failed but cannot identify where or why
- Secrets, permissions, and network access expand as new integrations are added
- Security controls differ between environments and workloads
- Scaling decisions are reactive and disconnected from actual usage patterns
- Cloud costs increase without enough visibility into which product behaviours are responsible
The result is a platform that runs the application but slows down the team responsible for improving it.
Who this is for
Startups and engineering teams that need a secure, maintainable AWS foundation for AI products and data-intensive applications.
- Startups preparing an AI prototype for production use
- Product teams building AI agents, copilots, or RAG systems on AWS
- Teams that need repeatable development, staging, and production environments
- Companies improving deployment reliability and release speed
- Engineering teams that lack visibility across application, retrieval, and model workloads
- Organisations that need stronger cloud security without creating unnecessary delivery friction
- Teams redesigning an AWS environment that has grown inconsistently over time
This is not a generic cloud migration or an infrastructure diagram delivered without implementation.
It is hands-on cloud platform engineering for teams building and operating production AI software.
Engineer in the loop
I treat the cloud platform as part of the product rather than a separate layer that is designed after the application.
Infrastructure should give developers a predictable way to build, test, deploy, observe, and recover their systems. It should make secure behaviour easier and operational problems visible before they become incidents.
I use infrastructure as code, automated delivery, and AI-assisted engineering workflows to move quickly while keeping architecture and security decisions explicit.
In practice, this means:
- Designing the application and platform architecture together
- Defining infrastructure as version-controlled, reviewable code
- Automating builds, tests, deployments, and rollback paths
- Applying identity, network, data, and secrets controls at clear system boundaries
- Instrumenting application, infrastructure, retrieval, and agent workflows as connected operations
- Keeping operational ownership with the engineering team that builds the product
The goal is a cloud platform that supports product development rather than becoming a second product the team has to fight.
Scope
AWS architecture
Account and environment design, networking, compute, storage, databases, queues, APIs, service boundaries, and deployment topology.
AI workload infrastructure
Platforms for agents, copilots, RAG pipelines, model APIs, background processing, vector search, knowledge systems, and multimodal workloads.
Containers & serverless
Amazon ECS, AWS Lambda, container images, task design, autoscaling, workload isolation, asynchronous processing, and runtime selection.
Infrastructure as code
Terraform, AWS CDK, or CloudFormation modules for repeatable environments, reviewable changes, policy enforcement, and controlled evolution.
CI/CD & release engineering
Automated builds, testing, image publishing, database migrations, deployment strategies, environment promotion, rollback, and release visibility.
Observability & operations
Structured logs, metrics, traces, dashboards, alerting, deployment health, failure investigation, audit events, and operational runbooks.
Cloud security
IAM, workload identity, secrets management, encryption, network boundaries, data protection, auditability, and least-privilege access.
Reliability & scale
Availability design, health checks, retries, queues, backpressure, scaling policies, failure isolation, recovery paths, and resilience testing.
Cost & efficiency
Workload sizing, scaling behaviour, storage choices, network costs, model usage visibility, budget controls, and architecture trade-offs.
Existing platform improvement
AWS architecture review, security hardening, deployment redesign, observability improvement, operational simplification, and cost analysis.
What you receive
The exact output depends on whether the engagement starts with a new product, an existing prototype, or an established AWS environment. It can include:
Production AWS platform
A working cloud environment for running, deploying, scaling, and operating the agreed AI or application workloads.
Platform architecture
A clear model of accounts, environments, networks, workloads, data stores, external services, and security boundaries.
Infrastructure as code
Version-controlled infrastructure modules that make environments repeatable, reviewable, and easier to evolve.
Deployment pipelines
Automated build, test, release, migration, deployment, verification, and rollback workflows.
Environment strategy
A practical development, testing, staging, and production model with controlled configuration and promotion between environments.
Security controls
Workload identities, IAM policies, secret handling, encryption, network restrictions, audit trails, and least-privilege access.
Observability foundation
Structured logging, metrics, dashboards, alerts, traces, health signals, and visibility into deployment and workload failures.
Reliability controls
Health checks, retries, queues, scaling policies, failure isolation, recovery paths, and resilience considerations.
Operational documentation
Architecture decisions, setup instructions, deployment guidance, troubleshooting steps, and ownership expectations.
Improvement roadmap
Prioritised follow-up work covering reliability, security, delivery, cost, maintainability, and future scale.
Work can also begin with a smaller, focused engagement:
- AWS architecture and security review
- Production-readiness assessment for an AI prototype
- CI/CD and deployment reliability improvement
- Observability and incident-diagnosis improvement
- Infrastructure-as-code implementation or refactoring
- Cloud cost and scaling analysis
The objective is a platform that developers can understand and operate, with enough structure to support growth without creating unnecessary complexity too early.
Background
I am a software engineer and architect with more than 20 years of experience building cloud platforms, distributed systems, developer tools, security controls, and production software.
My AWS work includes application and platform architecture, containerised workloads, serverless systems, infrastructure as code, CI/CD, observability, IAM, cloud security, resilience, and multi-environment delivery.
I combine that cloud background with hands-on work on AI agents, retrieval systems, knowledge graphs, and AI-enabled products. This allows the infrastructure to be designed around the actual behaviour of the product rather than as a generic hosting layer.
Building an AI product that needs a stronger AWS foundation?
Tell me what you are running, what already exists, and where delivery or operations are becoming difficult. I will reply directly and suggest a practical way to approach the platform.