Strategic AI Solutions Architect: Cloud-Native Data & LLM

Cuesta Partners; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Cuesta Partners seeks an AI Solutions Architect to bridge customers, delivery teams, and executives for end-to-end AI engagements. You will own the production solution design from discovery through handoff, ensuring scalable, secure, and financially viable outcomes.

You will lead architectural design across AI-enabled products and data platforms, mentor junior architects, and collaborate with ML/Data engineers on feasibility and integration.

Qualifications

• 8+ years in solution architecture, data engineering, or software engineering roles, with production AI/ML experience.
• Proven cloud-native AI solution design across AWS, Azure, or GCP with data platform integration, LLMs, and secure APIs.
• Hands-on experience with two of: DL frameworks, NLP/LLM stacks, CV pipelines, or AutoML/orchestration.

Responsibilities

• Design cloud-native AI and data solution architectures balancing scalability, security, cost, and value.
• Translate architectures into diagrams, narratives, visuals, and roadmaps for stakeholders.
• Advise clients on AI strategy, governance, and build-vs-buy decisions.
• Integrate data across platforms and external APIs; collaborate on EC-ready solutions.
• Mentor junior architects; develop reusable accelerators, templates, and knowledge base.
• Provide technical oversight across engagements ensuring architectural consistency.

Skills

Cloud-native AI

LLM orchestration

API design

Data engineering

Communication

Education

Bachelor's degree in CS/Engineering

Tools

Docker

Kubernetes

Terraform

Job description

Cuesta Partners seeks an AI Solutions Architect to bridge customers, delivery teams, and executives for end-to-end AI engagements. You will own the production solution design from discovery through handoff, ensuring scalable, secure, and financially viable outcomes.

You will lead architectural design across AI-enabled products and data platforms, mentor junior architects, and collaborate with ML/Data engineers on feasibility and integration.