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Microsoft

Builds workplace software, PCs and cloud services

Senior Applied AI Engineer

AI Application EngineeringConfirmed live in the last 24 hours
Full-time$119.8k - $261k /yrIn-person

About the Job

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.
  • 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, retrieval-augmented generation, 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 retrieval-augmented generation 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.
  • 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.

Responsibilities

  • Design and build scalable production agentic AI services using LLM orchestration, retrieval-augmented generation 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; build reusable components, reference implementations, and engineering patterns that accelerate team delivery.
  • Translate customer support scenarios and product requirements into technical designs, then lead 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 from experimentation to 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; balance 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.

Desired Qualifications

  • Background in customer support engineering, supportability, diagnostics, or customer-experience platforms.

About the Company

Microsoft

Builds workplace software, PCs and cloud services

Microsoft builds workplace software, operating systems and cloud infrastructure for consumers, businesses and government agencies, from small organizations to global enterprises. Microsoft 365 combines Word and Excel with Teams communication and Copilot AI features, while Windows runs PCs and Surface supplies the company's own computers. Azure rents computing capacity, storage and databases to build and run web applications. Microsoft also sells Xbox consoles and games, collecting revenue through software licenses, subscriptions, usage charges and product sales. Founded in 1975 by Bill Gates and Paul Allen, Microsoft is headquartered in Redmond, Washington.

Company size
10,001+
Company stage
IPO
Headquarters
Redmond, WA
Founded
1975

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