Hands-On Forward-Deployed AI Architect

Crowe; Austin (TX); Hybrid; USD 102,000 - 204,000; Full time

Job summary

Crowe Studio seeks a Forward Deployed AI Architect (Manager) to design, develop, and deliver production-ready AI solutions for clients. You will own solution architecture, feasibility, engineering quality, and production readiness while remaining actively involved in coding and prototyping.

You will collaborate with AI engineering, data engineering, MLOps, DevOps, cloud infrastructure, and security teams to deploy scalable, secure AI applications on Azure, AWS, or GCP.

Qualifications

• Bachelor's degree in a technical field required.
• Minimum 6 years of production software engineering, cloud engineering, or AI/ML experience.
• Proven ability to architect cloud-native apps and distributed systems.
• Strong client-facing communications and mentorship skills.

Responsibilities

• Architect and deliver end-to-end AI solutions, including generative AI and agentic systems.
• Lead architecture for AI build and PoV engagements, ensuring feasibility, scalability, and security.
• Remain hands-on across development, data integration, prototyping, and deployment.
• Define cloud-native architectures across Azure, AWS, GCP and related services.
• Mentor engineers, establish standards, and guide architectural tradeoffs with stakeholders.
• Collaborate with cross-functional teams to ensure production readiness and risk management.

Skills

Hands-on software engineering

Cloud-native architectures

Generative AI / LLM patterns

Architecture tradeoffs & design

Client-facing communication

Travel up to 80%

Education

Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field

Tools

Azure

AWS

GCP

Kubernetes

Terraform

RAG / vector databases

Job description

Crowe Studio seeks a Forward Deployed AI Architect (Manager) to design, develop, and deliver production-ready AI solutions for clients. You will own solution architecture, feasibility, engineering quality, and production readiness while remaining actively involved in coding and prototyping.

You will collaborate with AI engineering, data engineering, MLOps, DevOps, cloud infrastructure, and security teams to deploy scalable, secure AI applications on Azure, AWS, or GCP.