AI Solution architect

Role : AI Solutions Architect

Contract

Location: US Remote (Some onsite travel will be required)

Senior embedded cloud/AI technical lead role, with strong emphasis on GCP architecture, CI/CD, productionizing prototypes, customer-facing leadership, and generative AI

Required Skills:

1. GCP Architecture
2. CI/CD + DevOps
3. Prototype-to-production architecture
4. Generative AI / Vertex AI / Gemini
5. Security & IAM
6. System design
7. Customer-facing technical leadership
8. Terraform / Infrastructure as Code
9. Programming & API development
10. Mentorship and delivery leadership

Project Description

The customer is a manufacturing holding company operating several independent business units, each running its own production operations. The customer is working to build internal software engineering capability so that prototype applications developed internally can be matured into secure, production ready tools.

This engagement embeds an engineer directly within the customer’s team to review and prioritize its technical work and to then quickly design and prototype AI-driven solutions to illustrate to the potential outcomes.

Prototypes over time will then need to be mature and have the surrounding cloud environment configured, including CI/CD pipelines and integration points, needed to support this maturation. The role works within Google Cloud Platform, collaborating with both technical and business stakeholders to advance the customer’s generative AI initiatives from prototype to production.

Main Responsibilities:

Embedded Technical Ownership (works directly within the customer’s environment and owns technical direction for the engagement)

• Acts as the primary technical point of contact embedded with the customer’s team, reviewing and prioritizing the technical work.

• Operates independently on ambiguous or complex problems, escalating only high impact decisions.

Cloud Environment and CI/CD Design (designs and configures the technical foundation the engagement depends on)

• Designs the structure of the customer’s Google Cloud Platform environment, including how development and deployment workflows are organized.

• Establishes CI/CD pipelines, source control workflows, and release processes suited to the customer’s environment.

Prototype to Production Architecture (defines the technical path for taking prototype applications to a production ready state)

• Assesses existing prototype applications, including those built by non-technical business users, and defines the architectural changes needed to make them secure and production ready.

• Works alongside the customer’s own staff to implement these changes without displacing their ownership of the resulting systems.

• Use AI tools to design and prototype solutions that can solve business outcomes

Stakeholder Collaboration (works directly with technical and business stakeholders to keep the engagement aligned with customer priorities)

• Meets regularly with the business stakeholders who originated the prototype applications to understand use case context and constraints.

• Advises the customer on how architectural decisions support its broader generative AI initiatives.

Mentorship and Workstream Ownership (carries end to end accountability for the engagement’s technical output)

• Mentors additional engineering staff added to the engagement as work volume increases.

• Owns the technical quality and coherence of the engagement’s output as a single workstream.