Principal Applied AI Engineer

<b>Overview</b><br><div style="text-align: justify;"><span style="font-size: 10.0pt; font-family: verdana, geneva, sans-serif;">Are you a customer-obsessed software engineer and technical architect who is passionate about applying AI to complex, real-world problems? The Engineering Operations (EngOps) team's mission is to transform Microsoft Cloud customers into fans. Through our deep engineering engagements with customers and teams across Microsoft, we analyze and amplify customer needs and drive the vision to improve cloud quality, security, and reliability. Our culture of growth mindset and empowerment is central to who we are and how we work.</span></div><div style="text-align: justify;"> </div><div style="text-align: justify;"><span style="font-size: 10.0pt; font-family: verdana, geneva, sans-serif;">Our ACES CODE engineering team is building domain-specific agents and services that are fundamentally transforming Azure customer support, moving from reactive, human-only workflows to AI-first systems that resolve customer issues at Azure scale. We are seeking a Principal Applied AI Engineer to serve as a hands-on technical architect and developer for production agentic AI services.</span></div><div style="text-align: justify;"> </div><div style="text-align: justify;"><span style="font-size: 10.0pt; font-family: verdana, geneva, sans-serif;">You will define and implement the architecture for next-generation AI agents, including LLM orchestration, multi-agent coordination, retrieval-augmented generation (RAG), tool integration, and evaluation systems. You will write production code, lead complex technical initiatives, establish engineering standards, and guide engineers across organizational boundaries through technical influence rather than people management.</span></div><div style="text-align: justify;"><span style="font-size: 10.0pt; font-family: verdana, geneva, sans-serif;">This is a deeply technical individual contributor role with broad architectural scope and direct accountability for production outcomes. You will turn ambiguous customer and business problems into scalable systems, make critical design decisions, build foundational components, and help teams ship secure and reliable AI services that handle thousands of customer cases per month.</span></div><div style="text-align: justify;"><span style="font-size: 10.0pt; font-family: verdana, geneva, sans-serif;">Every day, our customers stake their business and reputation on our cloud. You can help EngOps ACES provide our customers with the world-class cloud services they need to succeed.</span></div><div style="text-align: justify;"><span style="font-size: 10.0pt; font-family: verdana, geneva, sans-serif;">Microsoft's mission is to empower every person and every organization on the planet to achieve more. As employees, we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day, we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond.</span></div><div style="text-align: justify;"> </div><br><br><b>Responsibilities</b><br><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Architect and build production agentic AI systems: Design and implement scalable AI agents and services, including LLM orchestration, multi-agent workflows, RAG pipelines, tool integrations, memory and state management, and human-in-the-loop experiences.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">Lead through hands-on engineering: Write, review, and maintain production-quality C# and Python code. Build critical components, reference implementations, prototypes, and reusable platform capabilities that accelerate delivery across the engineering organization.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Own end-to-end technical delivery: Translate ambiguous customer support scenarios into clear technical requirements and take solutions from architecture and experimentation through implementation, deployment, operation, and continuous improvement.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Define technical architecture and engineering standards: Establish architecture patterns, API and data contracts, code quality expectations, evaluation practices, deployment strategies, and observability standards for AI services. Drive technical consistency while allowing teams to evolve their solutions.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Advance evaluation-driven AI development: Design and implement evaluation frameworks using rubrics, golden datasets, simulation, judge models, and offline and online evaluation. Define quality thresholds and graduation criteria for moving agents from experimentation through shadow mode and into autonomous operation.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Own production quality and reliability: Ensure AI services meet demanding standards for correctness, availability, latency, scalability, cost, and safety. Diagnose complex production issues, lead technical incident response and root cause analysis, and engineer systemic improvements.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Design for responsible and secure AI: Embed responsible AI principles, privacy protections, prompt-injection defenses, tool authorization, data boundaries, action controls, auditability, and fail-safe behavior into system architecture and implementation.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Make consequential technical decisions: Evaluate models, frameworks, platforms, and architectural approaches using evidence from experiments and production telemetry. Balance solution quality with reliability, maintainability, latency, cost, and operational complexity.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Influence product and platform roadmaps: Partner with product managers, researchers, platform engineers, and Azure service teams to shape technical strategy and platform capabilities. Translate customer support patterns and operational signals into durable engineering investments.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Drive measurable customer and business impact: Instrument systems and use data to improve automation accuracy, case volume reduction, resolution time, engineering efficiency, and customer satisfaction. Connect architectural investments to measurable outcomes.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Provide technical leadership across teams: Lead architecture reviews, resolve complex technical disagreements, and align engineers and partner organizations around shared designs. Communicate clearly with engineering teams, leaders, and non-technical stakeholders.</span></p><p><span style="font-family:verdana, geneva, sans-serif;font-size:10.0pt">• Raise the engineering capability of the organization: Mentor engineers in system design, software engineering, applied AI, and evaluation-driven development. Create reusable guidance and help teams develop the judgment to determine where AI agents should and should not act autonomously.</span></p><br><br><b>Qualifications</b><br><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;"><strong>Basic Qualifications</strong></span></div><div> </div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Bachelor's degree in Computer Science, Engineering, or a related technical field.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• 12+ years of professional software engineering experience designing, building, and operating production software systems.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Proficiency in C# and/or Python, with demonstrated ability to design and implement production-quality services and distributed systems.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience serving as a technical architect or technical lead for complex, cross-team engineering initiatives.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience with applied AI systems, including LLM-based applications, prompt engineering, RAG architectures, agent frameworks, or machine learning services.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience with cloud platforms, preferably Azure, and cloud-native engineering practices including microservices, APIs, containers, CI/CD, infrastructure as code, and observability.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Demonstrated success owning the full engineering lifecycle, from problem definition and architecture through implementation, deployment, and production operations.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Profilency in software architecture, distributed systems, data structures, algorithms, API design, security, testing, and service reliability.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Proven ability to influence technical direction and align multiple teams without direct management authority.</span></div><div> </div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;"><strong>Preferred Qualifications</strong></span></div><div> </div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience architecting and shipping multi-agent orchestration systems or agentic AI services in production.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience with AI evaluation systems, including rubrics, golden datasets, judge models, simulation, adversarial testing, and offline and online evaluation.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience with Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, or similar enterprise AI platforms.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Deep knowledge of RAG design, including indexing, retrieval, ranking, grounding, citation, knowledge freshness, and access control.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience implementing agent security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience building highly available distributed services with rigorous standards for performance, scalability, resiliency, observability, and cost efficiency.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Experience leading architecture and co-development efforts involving multiple product, platform, research, and partner engineering teams.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Demonstrated ability to move between strategic architecture and detailed implementation, including debugging complex interactions across models, prompts, tools, data, and distributed services.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Business acumen and experience connecting technical investments to measurable customer outcomes such as case volume reduction, resolution time, automation quality, and customer satisfaction.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Track record of mentoring engineers and raising engineering standards across teams without formal people management responsibilities.</span></div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">• Background in customer support engineering, supportability, diagnostics, or customer experience platforms is a plus.</span></div><div> </div><div><span style="font-family: verdana, geneva, sans-serif; font-size: 10.0pt;">#EngOps #EngOpsACES</span></div><div> </div> <br><p>This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.</p><br><hr><br><p>Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more about <a href="https://careers.microsoft.com/v2/global/en/accessibility.html"><b><u>requesting accommodations.</u></b></a></p>