Solution Engineering Cloud & AI Apps

The AI & Apps Solution Engineer is a customer-facing technical sales professional responsible for helping customers modernize applications and adopt AI-powered development platforms. The role focuses on winning technical decisions, accelerating Azure consumption, and driving customer adoption of Microsoft AI technologies including Azure AI, AI Foundry, Agentic AI, GitHub Copilot, and modern application platforms.

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     Qualifications 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.   This position will be open for a