Principal Applied AI Engineer

Microsoft • Redmond, WA

3d • $142,800 - $304,200

About The Position

ACES CØDE is a Microsoft engineering organization focused on reducing operational demand across the Microsoft Cloud, including but not limited to the avoidable support cases, repeated troubleshooting, and manual interventions that arise when cloud products, platforms, or processes do not work as intended. Our mission is to engineer this demand out of the Microsoft Cloud by turning customer and operational signals into lasting product improvements, intelligent diagnostics, autonomous resolution systems, and AI-powered capabilities. As cloud services grow in scale and complexity, we are moving beyond simply reacting to problems after they occur. We work to understand why operational demand exists, prevent it at the source where possible, resolve it autonomously when it does occur, and continuously learn from those signals to make our products and support experiences more reliable. We look for teammates who can navigate ambiguity, reason from data and customer signals, work across technical and organizational boundaries, and turn complex problems into durable, measurable outcomes.
Within this mission, the ACES CØDE engineering team is building domain-specific agents and services that are transforming Azure customer support from reactive, human-dependent workflows toward AI-first systems capable of resolving customer issues at Azure scale. We are seeking a Principal Applied AI Engineer to serve as a hands-on technical architect and senior developer for these production agentic AI services.
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.
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.
Every day, our customers stake their business and reputation on our cloud. You can help Azure EngOps provide our customers with the world-class cloud services they need to succeed.
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.

Requirements

• Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• Ability to meet Microsoft, customer and/or government security screening requirements are required for this role. These requirements include, but are not limited to, the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter.

Nice To Haves

• 15 years of professional software engineering and enterprise architecture experience designing, building, integrating, and operating complex production software systems at enterprise scale.
• Master's Degree in Computer Science or related technical field AND 8+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR Bachelor's Degree in Computer Science or related technical field AND 12+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
• Deep hands-on software development background combined with enterprise architecture expertise, with demonstrated ability to translate business and technical requirements into scalable, resilient, and maintainable system architectures.
• Proficiency in C# and/or Python, with demonstrated ability to design and implement production-quality services and distributed systems.
• Experience with applied AI systems, including LLM-based applications, prompt engineering, RAG architectures, agent frameworks, or machine learning services.
• Experience with cloud platforms, preferably Azure, and cloud-native engineering practices including microservices, APIs, containers, CI/CD, infrastructure as code, and observability.
• Proven ability to influence technical direction and align multiple teams without direct management authority.
• Experience implementing agent security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.
• Demonstrated ability to move between strategic architecture and detailed implementation, including debugging complex interactions across models, prompts, tools, data, and distributed services.
• Proven business acumen and experience connecting technical investments to measurable customer outcomes such as case volume reduction, resolution time, automation quality, and customer satisfaction.
• Track record of mentoring senior engineers and raising engineering standards across teams without formal people management responsibilities.
• Background in customer support engineering, supportability, diagnostics, or customer experience platforms is a plus.

Responsibilities

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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, senior leaders, and non-technical stakeholders.
• 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.

Benefits

• Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here: https://careers.microsoft.com/us/en/us-corporate-pay

What This Job Offers

• Job Type: Full-time
• Career Level: Principal