Senior Applied AI Engineer

Microsoft

Confirmed live today High trust

Quick summary

Work type: On-site
Location: Redmond, WAAtlanta, GA
Salary: $119,800–$234,700 / yr
Posted: 1 day ago
Freshness: Confirmed live today
Closes: Apr 11, 2027
Nearby: 99+ roles within 25 mi

Market check

Salary context

Competitive pay

How this pay compares to similar roles

Similar $197k

This role $177k

$106k $250k

below market most similar roles pay here above market

This role pays less than 55% of similar roles. Most pay $157,500–$235,750 — the blue band above. At the midpoint, this role pays about $177k versus about $197k for comparable roles.

Based on 240 similar postings.

Employer

About Microsoft

Microsoft Corporation is a global technology leader producing software, hardware, and cloud services including Windows, Office 365, Azure cloud platform, Xbox gaming, and Surface devices. Industry: Software & Cloud Computing

Microsoft currently has 661 open roles on FindRole.

Listed pay typically runs $119,800–$234,700 across 633 roles with salary data.

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At a glance

TL;DR · Senior Applied AI Engineer

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The Senior Applied AI Engineer works across product, support, and engineering teams to translate customer problems into scalable technical solutions for Azure customers. You will design and build production AI services, specifically developing agentic AI solutions for diagnostics and documentation. Key responsibilities include building RAG pipelines, agent workflows, and evaluation frameworks using rubrics and golden datasets. You will write and maintain production-quality C# and Python code while integrating with Azure services like Azure OpenAI, Azure Functions, and Cosmos DB. The role requires implementing secure AI patterns, including prompt-injection defenses and least-privilege tool access. You will manage end-to-end delivery, from technical design to CI/CD deployment, ensuring services meet reliability, latency, and cost requirements while mentoring other engineers on applied AI and production software engineering.

Skills

Python C# LLM RAG Azure OpenAI Azure AI Foundry Azure Functions Cosmos DB CI/CD Agentic AI Prompt Engineering Infrastructure as Code Container Orchestration Automated Testing Observability Java C++ JavaScript

What you'll do

Analyze support-case trends and technical signals to identify recurring failure patterns and documentation gaps.
Develop scalable agentic AI solutions using LLM orchestration, RAG pipelines, and human-in-the-loop experiences.
Write, review, and maintain production-quality C# and Python code for reusable components and engineering patterns.
Translate customer support scenarios into technical designs and manage the full lifecycle from implementation to deployment.
Build evaluation frameworks using rubrics, golden datasets, and judge models to guide AI agents toward production.
Implement secure AI patterns including prompt-injection defenses, least-privilege tool access, and data boundary enforcement.
Operate production services using CI/CD, automated testing, feature flags, tracing, and incident response protocols.
Lead design reviews and provide technical mentorship to other engineers on applied AI and production software engineering.

What we're looking for

Bachelor's Degree in Computer Science or related technical field.
4+ years of technical engineering experience with coding in languages including C, C++, C#, Java, JavaScript, or Python.
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, or Cosmos DB.
Experience with CI/CD pipelines, automated testing, and production observability.
Experience designing AI evaluation systems using rubrics, golden datasets, judge models, and simulations.
Experience with RAG systems including indexing, retrieval, ranking, grounding, and access control.
Experience implementing agent-security patterns and building reliable distributed services.
Experience with infrastructure as code, container orchestration, and staged production deployment.
Demonstrated ability to lead technical execution, influence design decisions, and mentor engineers.
Background in customer support engineering, supportability, diagnostics, or customer-experience platforms (preferred).

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