Über diese AI Solution Architect Stelle bei PwC
Job Description & Summary
We are looking for an AI Solution Architect to join the CEE IT Digital Solutions team and lead the design of enterprise AI solutions across the Microsoft ecosystem.You will define architecture standards, engineering best practices, and reusable delivery patterns that enable teams to build secure, scalable, maintainable, and cost-effective AI solutions. Working closely with various people from developers, business analysts, project managers, security teams, to business stakeholders, you will help shape technology decisions and accelerate AI adoption across the organization.
This is a hands-on role requiring expertise across the full AI spectrum, from low-code solutions built with Copilot Studio and Power Platform to enterprise-grade AI applications built on Azure AI services and modern AI frameworks. You will help determine the right architecture, technology stack, and delivery approach for each business scenario.
What You Will Do
Define AI standards, reference architectures, and reusable design patterns that accelerate consistent, high-quality solution delivery.
Design scalable, secure, and cost-efficient AI architectures and integration strategies.
Design enterprise AI solutions and agentic architectures, including RAG capabilities, voice-based agents, intelligent automation, multi-agent systems, orchestration patterns, and Human-in-the-Loop processes.
Guide architecture decisions across Microsoft AI technologies such as Copilot Studio, Azure AI Foundry, Azure OpenAI, and modern AI development frameworks.
Remain hands-on through prototyping, proof-of-concepts, technical reviews, and key implementation decisions, defining approaches for MVP delivery, production deployment, and solution scaling.
Define AI testing, evaluation, monitoring, quality assurance, and success measurement approaches.
Collaborate with governance, security, and risk teams to ensure compliance with enterprise standards and responsible AI requirements.
Support solution shaping, technology selection, effort estimation, and technology adoption decisions, including evaluation of emerging AI capabilities and platforms.
Nice To Have
Power Platform ecosystem knowledge
Azure DevOps and CI/CD pipeline experience.
Microsoft Graph API.
LangChain, LangGraph, AutoGen, Microsoft Agent Framework, or similar frameworks.
Vector databases such as Qdrant or ChromaDB.
Databricks, or Microsoft Fabric.
Containers, Docker, Kubernetes.
Voice AI and conversational AI platforms.
Experience with frameworks such as FastAPI, Spring Boot, Express.js, or similar.
Mentor team members and help grow AI engineering capability across the organization.
What We Are Looking For
Required Experience
8+ years of experience in software engineering, solution architecture, or enterprise application development.
4+ years of experience designing and delivering AI, GenAI, intelligent automation, or machine learning solutions.
Strong experience with Copilot Studio, Microsoft Foundry, Azure OpenAI, and Azure AI Search.
Strong understanding of Copilot Studio and enterprise AI solution design.
Solid experience with the Azure platform, including application hosting, integration, security, identity, networking, storage, and data services.
Proficiency in Python or other programming languages and experience with modern software development practices.
Experience designing and integrating REST APIs and enterprise applications.
Hands-on experience with SQL and relational databases.
Experience with GitHub and source control best practices.
Strong understanding of LLMs, Generative AI, prompt engineering, vector search, embeddings, and RAG architectures.
Experience with agent-based architectures, orchestration frameworks, and Human-in-the-Loop solutions.
Knowledge of AI evaluation methods, quality measurements, monitoring, and observability practices.
Understanding of Responsible AI, AI governance, and AI cost optimization principles.
Strong understanding of AI-assisted software development practices, including the effective use of tools such as M365 Copilot, Cursor AI, Claude Code, and similar AI-powered engineering assistants.
Decision-Making Capabilities
You should be comfortable making architecture decisions across:
Low-code vs pro-code approaches.
Copilot Studio vs custom AI solutions.
Single-agent vs multi-agent architectures.
Human-in-the-Loop vs autonomous workflows.
Trade-offs between speed, scalability, security, and cost.
Soft Skills
Excellent communication and stakeholder management skills.
Ability to communicate effectively with both technical and business audiences.
Fluent written and spoken English.
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