Principal Applied AI Engineer

Microsoft•Redmond, WA

About The Position

The Engineering Operations (EngOps) team's mission is to transform Microsoft Cloud customers into fans. Through deep engineering engagements with customers and teams across Microsoft, they analyze and amplify customer needs and drive the vision to improve cloud quality, security, and reliability. Their culture of growth mindset and empowerment is central to who they are and how they work. The 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. This role is for a Principal Applied AI Engineer to serve as a hands-on technical architect and senior developer for production agentic AI services. The individual 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. They 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. The individual 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, customers stake their business and reputation on Microsoft's cloud. This role helps Azure EngOps provide customers with 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, they come together with a growth mindset, innovate to empower others, and collaborate to realize shared goals. Each day, they build on 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.

Nice To Haves

8+ years of professional software engineering experience designing, building, and operating production software systems.
Strong 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.
Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or a related technical field.
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.
Strong 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 strong 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.