Senior Applied AI Engineer

Microsoft•Redmond, WA

23h•$119,800 - $234,700

About The Position

ACES CØDE is a Microsoft engineering organization focused on reducing operational demand across the Microsoft Cloud by turning customer and operational signals into product improvements, intelligent diagnostics, autonomous resolution systems, and AI-powered capabilities. The ACES CØDE engineering team is building domain-specific agents and intelligent services to transform Azure customer support and product quality from reactive, human-only workflows toward AI-first systems that resolve customer issues and product repairs at Azure scale. The Senior Applied AI Engineer designs, builds, and operates production agentic AI services for AI-first support at Azure scale. The role uses Azure product knowledge and customer-support experience to identify recurring customer issues, investigate root causes, and determine whether diagnostics, clearer guidance, or AI agents will provide the greatest customer benefit. Working across product, support, and engineering teams, the engineer helps prioritize work, translates customer and business problems into scalable technical solutions, and delivers measurable customer impact. The role writes production-quality code, owns significant service components, leads technical execution within projects, and helps other engineers adopt effective software and applied AI engineering practices.

Requirements

Bachelor's degree in Computer Science or a related technical field and 4+ years of technical engineering experience coding in languages including C, C++, C#, Java, JavaScript, or Python, or equivalent experience.
Ability to meet Microsoft, customer, and/or government security screening requirements, including passing the Microsoft Cloud Background Check upon hire or transfer and every two years thereafter.

Nice To Haves

Experience building or integrating with LLM-based systems, including prompt engineering, RAG, or agent frameworks.
Familiarity with Azure services such as Azure OpenAI, Azure AI Foundry, Azure Functions, Cosmos DB, or similar cloud-native technologies.
Experience with CI/CD pipelines, automated testing, and production observability.
Experience with Azure AI Foundry, Azure OpenAI, Microsoft Agent Framework, or comparable enterprise AI platforms.
Experience designing AI evaluation systems using rubrics, golden datasets, judge models, simulation, adversarial testing, and offline or online evaluation.
Experience with RAG systems, including indexing, retrieval, ranking, grounding, citation, knowledge freshness, and access control.
Experience implementing agent-security patterns such as prompt-injection defense, identity propagation, least-privilege tool access, data-boundary enforcement, approval gates, and auditable actions.
Experience building reliable distributed services with strong standards for scalability, resiliency, performance, observability, and cost efficiency.
Experience with infrastructure as code, container orchestration, feature management, and staged production deployment.
Demonstrated ability to lead technical execution, influence design decisions, and mentor engineers without formal people-management authority.
Experience collaborating with product, platform, research, and partner engineering teams on complex co-development initiatives.
Strong business judgment and experience connecting engineering investments to customer and operational outcomes.
Background in customer support engineering, supportability, diagnostics, or customer-experience platforms is a plus.

Responsibilities

Analyze support-case trends, customer issue patterns, and authorized technical signals at scale to uncover recurring failure patterns and gaps in Azure diagnostics and documentation.
Develop scalable agentic AI solutions for Azure diagnostics and documentation, including LLM orchestration, RAG pipelines, agent workflows, tool integrations, memory and state management, and human-in-the-loop experiences.
Write, review, test, and maintain production-quality C# and Python code, and build reusable components, reference implementations, and engineering patterns.
Translate customer-support scenarios and product requirements into technical designs and deliver solutions through implementation, testing, deployment, monitoring, and continuous improvement.
Develop evaluation frameworks using rubrics, golden datasets, simulations, judge models, and offline and online evaluation; use measurable quality thresholds to guide agents toward production operation.
Ensure services meet requirements for correctness, reliability, availability, latency, scalability, maintainability, cost, and safety.
Apply privacy protections, prompt-injection defenses, least-privilege tool access, identity and authorization controls, data boundaries, approval gates, auditability, and fail-safe behavior.
Integrate Azure data sources, APIs, and cloud-native services to ground agent responses and actions in trusted, current signals.
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.
Evaluate models, frameworks, prompts, retrieval approaches, and architectural options using experiments and production telemetry, balancing quality with reliability, latency, cost, maintainability, and operational complexity.
Instrument solutions and use data to improve automation quality, case-volume reduction, resolution time, engineering efficiency, and customer satisfaction.
Lead design reviews, resolve technical challenges, coordinate implementation across contributors, and align partner teams around shared interfaces and delivery goals.
Support other engineers through code reviews, technical guidance, reusable documentation, and practical coaching in applied AI and production software engineering.
Partner with product managers, researchers, platform engineers, Azure service teams, and support-domain experts to deliver durable solutions and shape future platform capabilities.