Solution Engineering Cloud & AI Apps

Key Responsibilities 

Drive AI Application Transformation 

• Help customers design and implement AI-native applications and agents. 

• Lead conversations around AI Foundry, Azure AI Services, Azure OpenAI, Responsible AI, and application modernization. 

• Transform legacy application estates into modern, cloud-native, AI-powered architectures.  

Win Technical Decisions 

• Conduct technical workshops, architecture design sessions, hackathons, Proof of Concepts (POCs), and MVP engagements. 

• Validate solution architectures and remove technical blockers. 

• Influence platform and engineering leaders on technology decisions  

Solution Architecture & Technical Leadership 

• Design secure, scalable, enterprise-grade AI and application solutions. 

• Provide guidance on cloud-native development, APIs, microservices, containerization, event-driven architectures, and AI Operations. 

• Advocate Responsible AI and governance practices.   

Revenue & Consumption Growth 

• Accelerate pipeline progression from Uncommitted to Committed (UC→C). 

• Support Azure consumption growth and workload expansion. 

• Drive technical validation that leads to Azure AI and application platform adoption.   

Cross-Team Collaboration 

• Partner with Specialists, Account Teams (ATU), Customer Success Units (CSU), Global Black Belts (GBB), Engineering, and Partners. 

• Ensure smooth transition from technical validation to deployment and consumption.   

Technical Expertise Expected 

AI & Agentic AI 

• Azure AI Foundry 

• Azure OpenAI Service 

• Agentic AI architectures 

• Semantic Kernel 

• Responsible AI 

• LLM orchestration frameworks 

• Multi-agent systems   

Application Development 

• .NET, Java, Python 

• APIs and integrations 

• Containers & Kubernetes 

• Microservices 

• Event-driven architectures 

• DevOps and CI/CD pipelines   

Platform & Security 

• AI Security and Governance 

• GenAIOps 

• Azure Monitoring 

• Microsoft Defender 

• Azure Landing Zones 

• Enterprise architecture patterns   

Success Measures 

Typical success metrics include: 

• Technical wins achieved 

• Azure AI and Apps consumption growth 

• Pipeline acceleration (UC→C) 

• AI Foundry adoption 

• AI agent deployments 

• Customer architecture endorsements 

• Technical workshop and POC execution 

• Expansion of AI workloads within strategic accounts   

Ideal Candidate Profile 

• 5+ years of technical pre-sales, solution architecture, or consulting experience. 

• Strong application development and cloud architecture background. 

• Ability to translate business outcomes into technical solutions. 

• Hands-on experience with AI/ML and modern software development practices. 

• Executive-level communication and customer advisory skills. https://microsoftapc-my.sharepoint.com/personal/nirmpur_microsoft_com/_layouts/15/Doc.aspx?sourcedoc={519BC9D2-3F11-40B6-84B4-18A1DBEB3BCB}&file=Job description App SE.docx&action=default&mobileredirect=true&DefaultItemOpen=1  

What Great Looks Like 

An outstanding AI & Apps Solution Engineer: 

• Wins technical decisions through architecture leadership. 

• Demonstrates deep expertise in AI Foundry, Azure AI, and application modernization. 

• Delivers impactful workshops, hackathons, and POCs. 

• Influences CTOs, Engineering Leaders, and Platform Teams. 

• Converts technical engagement into measurable Azure consumption and business outcomes. 

• Acts as a trusted advisor helping customers become AI-native organizations.