Senior Applied AI Engineer

Microsoft•Redmond, WA

About The Position

The Senior Applied AI Engineer is a hands-on individual contributor on Microsoft’s Azure Engineering Operations (EngOps) ACES CODE engineering team. The team builds domain-specific agents and intelligent services to transform Azure customer support and product quality, shifting reactive, human-only workflows toward AI-first systems that resolve customer issues and product repairs at Azure scale. In this role, you will translate customer and business problems into scalable technical solutions and design, build, and operate production agentic AI services. The work includes LLM orchestration, retrieval-augmented generation (RAG), agent workflows, tool integration, AI evaluation, and integration with Azure services and data sources. You will own significant service components, lead technical execution within projects, deliver solutions through deployment and operation, and help other engineers adopt sound software and applied AI engineering practices. You will collaborate with product managers, researchers, platform engineers, Azure service teams, and support-domain experts to deliver reliable, secure, measurable customer outcomes.

Requirements

Bachelor’s degree in Computer Science or a related technical field and 4+ years of technical engineering experience coding in languages such as 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

Familiarity with Azure services such as Azure OpenAI, Azure AI Foundry, Azure Functions, Cosmos DB, or similar cloud-native technologies.
Experience building or integrating with LLM-based systems, including prompt engineering, RAG, or agent frameworks.
Experience with CI/CD pipelines, automated testing, and production observability.
Strong understanding of data structures, algorithms, and system design fundamentals.
Experience building and shipping agentic AI services or multi-agent workflows in production.
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.

Responsibilities

Design and build scalable production agentic AI services using 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; create 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 AI evaluation frameworks using rubrics, golden datasets, simulations, judge models, and offline and online evaluation; use measurable quality thresholds to guide production readiness.
Engineer services for correctness, reliability, availability, latency, scalability, maintainability, cost, and safety.
Apply secure and responsible AI practices, including 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 so agent responses and actions are grounded in trusted, current information.
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 through 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.
Mentor engineers through code reviews, technical guidance, reusable documentation, and practical coaching in applied AI and production software engineering.
Collaborate with product managers, researchers, platform engineers, Azure service teams, and support-domain experts to deliver durable solutions and shape future platform capabilities.