Senior Solutions Engineer - Developer Productivity & AI

What matters most

• Driving Adoption of Agentic SDLC

  The core mandate is to guide enterprise customers in transitioning from traditional development workflows to Agentic SDLC practices. You must demonstrate how AI agents can be integrated into planning, coding, testing, and deployment while maintaining human oversight and governance.

• Leading Hands-on Technical Engagements

  You are expected to move beyond theoretical advice by facilitating hackathons, code-with sessions, and architecture workshops. Success is measured by your ability to lead these sessions to progress customers from initial experimentation to production-scale adoption.

• Architecting Secure AI-Powered Solutions

  You must co-design scalable, secure architectures that leverage Microsoft's developer platforms, including GitHub and Azure AI Foundry. This requires balancing the implementation of AI-assisted coding tools with enterprise-grade security and governance requirements.

• Bridging Customer Feedback to Product Teams

  As a technical advisor, you must resolve blockers and synthesize customer feedback. You are responsible for communicating these insights back to Microsoft product and engineering teams to inform future product improvements and accelerate platform adoption.

Likely challenges to think through

• Balancing Innovation with Enterprise Governance

  Customers may be hesitant to adopt agentic workflows due to security or compliance concerns. Prepare to articulate how to implement AI agents while maintaining rigorous human oversight and enterprise-grade governance, addressing potential skepticism from security-focused stakeholders.

• Translating Technical Value to Business Outcomes

  You will need to connect abstract productivity gains from AI tools to concrete business metrics like delivery speed and software quality. Prepare to define and measure success criteria that resonate with both technical decision-makers and business leaders.

• Managing Diverse Stakeholder Expectations

  Engagements involve a mix of developers, platform leads, and executives. You must be able to pivot between deep-dive technical discussions on CI/CD and API integration to high-level strategic conversations regarding the long-term impact of AI on the software development lifecycle.

Topics to prepare

• Agentic SDLC Concepts

  Review how AI agents can be integrated into planning, coding, testing, and deployment workflows. Exercise: Map out a traditional CI/CD pipeline and identify specific stages where an autonomous agent could provide value, while defining the necessary human oversight and governance guardrails.

• Enterprise AI Governance

  Study the balance between developer productivity and enterprise security. Exercise: Draft a framework for implementing AI-assisted coding tools in a regulated environment, focusing on how to maintain auditability, data privacy, and compliance while scaling adoption across large engineering teams.

Skills to revise

• GitHub Actions & Administration

  Master the configuration of automated workflows and repository management. Exercise: Create a complex GitHub Actions workflow that triggers on a pull request, performs automated security scanning, and integrates with an external API to validate code quality metrics.

• Azure AI Foundry Integration

  Gain proficiency in building and deploying AI-powered applications. Exercise: Build a small-scale prototype using Azure AI Foundry SDKs that demonstrates how to connect a custom AI agent to a development toolchain, ensuring it follows secure architecture patterns.

• Technical Pre-Sales Engagement

  Practice the art of demonstrating technical value to decision-makers. Exercise: Prepare a 15-minute mock presentation for an engineering leader that connects the adoption of AI-assisted coding tools to specific business outcomes like reduced cycle time and improved software quality.

• Develop an Agentic SDLC Case Study

  Prepare a narrative or project walkthrough demonstrating how you have integrated AI-assisted coding tools (like GitHub Copilot) or AI agents into a software development lifecycle. Focus on how you balanced automation with human oversight, governance, and security, specifically addressing how you measured improvements in developer productivity or delivery speed.

• Map Technical Expertise to Microsoft Platforms

  Create a summary document mapping your experience with CI/CD, DevOps, and cloud-native architectures to Microsoft's specific stack, including GitHub Actions, Azure AI Foundry, and Visual Studio. Highlight instances where you resolved technical blockers for enterprise engineering teams.

• Design a Hands-on Engagement Framework

  Outline a plan for leading a technical workshop or hackathon. Define the structure of a Proof of Concept (PoC) that moves a customer from experimentation to production-scale adoption of AI-powered applications, including how you would define and track success criteria.

• Prepare Strategic Questions for the Interviewer

  Formulate thoughtful questions regarding the current challenges enterprise customers face when adopting Agentic SDLC practices. Ask about the balance between rapid AI adoption and maintaining enterprise-grade security and governance within the Microsoft ecosystem.