Senior Applied AI Engineer

** Overview*
* Are you a customer-obsessed software engineer who is passionate about using AI to solve complex, real-world problems? The Azure Engineering Operations (Eng Ops) team's mission is to transform Microsoft Cloud customers into fans. Through deep engineering engagements with customers and teams across Microsoft, we analyze and amplify customer needs and drive improvements in cloud quality, security, and reliability. Our culture of growth mindset and empowerment is central to who we are and how we work.

Our ACES CODE engineering team is building domain-specific agents and intelligent services that are transforming Azure customer support and product quality, moving from reactive, human-only workflows toward AI-first systems that resolve customer issues and product repairs at Azure scale. We are seeking a Senior Applied AI Engineer to design, build, and operate production agentic AI services.

In this hands-on individual contributor role, you will translate customer and business problems into scalable technical solutions involving LLM orchestration, retrieval-augmented generation, agent workflows, tool integration, and AI evaluation. You will write production-quality code, own significant service components, lead technical execution within projects, and help other engineers adopt sound software and applied AI engineering practices.

Every day, our customers stake their businesses and reputations on our cloud. You can help Azure Eng Ops provide 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.

** Responsibilities*
* Design and build production AI services:
Develop scalable agentic AI solutions using LLM orchestration, RAG pipelines, agent workflows, tool integrations, memory and state management, and human-in-the-loop experiences.

-    Lead through hands-on engineering:
Write, review, test, and maintain production-quality C# and Python code. Build reusable components, reference implementations, and engineering patterns that accelerate team delivery.

-    Own end-to-end technical delivery:
Translate customer support scenarios and product requirements into technical designs, then take solutions through implementation, testing, deployment, monitoring, and continuous improvement.

-    Build evaluation-driven AI systems:
Develop evaluation frameworks using rubrics, golden datasets, simulations, judge models, and offline and online evaluation. Use measurable quality thresholds to guide agents from experimentation toward production operation.

-    Engineer for production quality:
Ensure services meet requirements for correctness, reliability, availability, latency, scalability, maintainability, cost, and safety.

-    Implement secure and responsible AI patterns:
Apply privacy protections, prompt-injection defenses, least-privilege tool access, identity and authorization controls, data boundaries, approval gates, auditability, and fail-safe behavior.

-    Integrate with Azure services and data sources:
Ground agent responses and actions in trusted, current signals by integrating with Azure data sources, APIs, and cloud-native services.

-    Operate services in production:
Implement CI/CD, automated testing, feature flags, staged rollouts, tracing, logging, metrics, and alerting. Diagnose production issues, participate in incident response, and drive corrective improvements.

-    Make evidence-based technical decisions:
Evaluate models, frameworks, prompts, retrieval approaches, and architectural options using experiments and production telemetry. Balance quality with reliability, latency, cost, maintainability, and operational complexity.

-    Drive measurable customer impact:
Instrument solutions and use data to improve automation quality, case-volume reduction, resolution time, engineering efficiency, and customer satisfaction.

-    Provide technical leadership within projects:
Lead design reviews, resolve technical challenges, coordinate implementation across contributors, and align partner teams around shared interfaces and delivery goals.

-    Mentor and raise engineering capability:
Support other engineers through code reviews, technical guidance,…