Cloud Solution Architect, AI Apps

Microsoft

Confirmed live yesterday High trust

Quick summary

Work type: On-site
Location: —
Salary: $86,100–$169,800 / yr
Posted: 63 days ago
Freshness: Confirmed live yesterday
Closes: Jan 5, 2027

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $182k

This role $128k

$69k most similar roles pay here $242k

This role pays less than 85% of similar roles. Most pay $149,500–$214,850 — the shaded band above. At the midpoint, this role pays about $128k versus about $182k for comparable roles.

Based on 239 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 598 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 586 roles with salary data.

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At a glance

TL;DR · Cloud Solution Architect, AI Apps

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Cloud Solution Architect - AI Apps joins the Customer Experience and Success team to help customers design, build, and scale production-grade AI applications and intelligent agents. This role involves leading technical engagements to translate complex business challenges into scalable solutions while providing thought leadership on agentic systems and modern AI development. You will develop end-to-end architectures, create reusable assets like accelerators and patterns, and provide feedback to Engineering to influence product roadmaps. Key technical requirements include proficiency in Python, C#, or JavaScript, along with experience in Azure services, Kubernetes, and API Management. The role demands expertise in RAG, tool calling, multi-agent workflows, and MLOps practices. You will also utilize GitHub Copilot and AI-assisted workflows to manage the full lifecycle of AI systems, ensuring production readiness through robust CI/CD pipelines and responsible AI principles.

Skills

Generative AI Agentic Systems Azure Python C# JavaScript Kubernetes AKS CI/CD MLOps LLMOps RAG GitHub Copilot API Management Event-Driven Architecture AWS GCP

What you'll do

Design and deliver production-grade AI applications and agent-based solutions that provide measurable business value for customers.
Translate complex business requirements into scalable technical architectures from initial proof-of-concept to enterprise deployment.
Develop reusable assets, patterns, and intellectual property to accelerate delivery velocity and reduce time to production.
Provide structured feedback to Engineering teams to influence product roadmaps and feature prioritization for AI tools.
Act as a technical thought leader by sharing expertise on agentic systems and modern design patterns with internal teams.
Implement engineering best practices including CI/CD, test automation, model evaluation, and secure deployment for AI systems.
Utilize AI-assisted development workflows and tools like GitHub Copilot to accelerate prototyping and code review.
Identify opportunities to expand customer adoption of AI applications across their entire technology estate.

What we're looking for

Bachelor's degree in Computer Science, IT, Engineering, Business, Liberal Arts, or a related field.
At least 2 years of experience in cloud/infrastructure technologies, IT consulting, systems administration, network operations, software development, or architecture.
Ability to pass the Microsoft Cloud Background Check and other required security screenings.
Proficiency in modern programming languages such as Python, C#, or JavaScript for building AI-powered applications.
Experience integrating AI solutions with Azure services like Functions, AKS/Kubernetes, and API Management.
Proven ability to design end-to-end AI application architectures including prompt design, grounding strategies, and evaluation techniques.
Solid understanding of DevOps, MLOps, and LLMOps practices for operationalizing AI applications in production environments.
Understanding of Responsible AI principles, including safety, bias mitigation, and ethical considerations.

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