AI Orchestration Architect for Process Automation
Budget: $750 – $1,500 USD
I need a senior-level architect to design and build an end-to-end AI orchestration and enterprise-grade integration layer that genuinely streamlines our business processes. The role goes well beyond proof-of-concept work: I’m looking for a complete, production-ready blueprint and the initial implementation that can scale across multiple business units.
You will start by mapping current workflows and data flows, then propose a target architecture that we can govern centrally yet deploy in a modular fashion. Expect to work with event-driven micro-services, API gateways, message queues, and orchestration frameworks such as Airflow, Kubeflow, Argo, or comparable platforms—open-source or commercial isn’t the issue; operational robustness is. Integration with our existing ERP, CRM, and data warehouse is essential, so experience with REST/GraphQL, ESB patterns, and real-time streaming (Kafka, Pulsar, or similar) will be key.
Once the architecture is approved, I’ll need a working reference implementation that includes:
• A clearly documented solution blueprint (Visio, Draw.io, or similar)
• Infrastructure-as-Code scripts (Terraform or CloudFormation) for repeatable deployments
• CI/CD pipeline definitions and automated test suites
• A small but functional set of orchestrated workflows demonstrating tangible process-time reductions
• Runbooks and operational handover documentation
Acceptance criteria:
1. Demonstrated reduction of at least one targeted process cycle time by 30 % in a controlled test.
2. All code, scripts, and diagrams stored in our private Git repository with clean commit history.
3. Deploys successfully in both staging and production-like environments using container orchestration (Kubernetes or OpenShift).
4. Comprehensive documentation enabling another engineer to extend the solution without hand-holding.
If you have led similar enterprise builds and can point to measurable business impact, I’d like to hear how you would tackle this challenge and what tooling stack you recommend.
You will start by mapping current workflows and data flows, then propose a target architecture that we can govern centrally yet deploy in a modular fashion. Expect to work with event-driven micro-services, API gateways, message queues, and orchestration frameworks such as Airflow, Kubeflow, Argo, or comparable platforms—open-source or commercial isn’t the issue; operational robustness is. Integration with our existing ERP, CRM, and data warehouse is essential, so experience with REST/GraphQL, ESB patterns, and real-time streaming (Kafka, Pulsar, or similar) will be key.
Once the architecture is approved, I’ll need a working reference implementation that includes:
• A clearly documented solution blueprint (Visio, Draw.io, or similar)
• Infrastructure-as-Code scripts (Terraform or CloudFormation) for repeatable deployments
• CI/CD pipeline definitions and automated test suites
• A small but functional set of orchestrated workflows demonstrating tangible process-time reductions
• Runbooks and operational handover documentation
Acceptance criteria:
1. Demonstrated reduction of at least one targeted process cycle time by 30 % in a controlled test.
2. All code, scripts, and diagrams stored in our private Git repository with clean commit history.
3. Deploys successfully in both staging and production-like environments using container orchestration (Kubernetes or OpenShift).
4. Comprehensive documentation enabling another engineer to extend the solution without hand-holding.
If you have led similar enterprise builds and can point to measurable business impact, I’d like to hear how you would tackle this challenge and what tooling stack you recommend.