below marketmost similar roles pay hereabove market
This role pays less than
58%
of similar roles. Most pay
$162,000–$235,750
— the blue band above.
At the midpoint, this role pays about
$177k
versus about
$199k
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
694 open roles
on FindRole.
Listed pay typically runs
$119,800–$234,700
across 651 roles with salary data.
The Senior Applied AI Engineer joins the team to design and build production AI services, specifically focusing on scalable agentic AI solutions. The role involves developing LLM orchestration, RAG pipelines, agent workflows, and human-in-the-loop experiences. Key responsibilities include writing production-quality C# and Python code, building reusable components, and translating customer support scenarios into technical designs. The engineer will develop evaluation frameworks using rubrics, golden datasets, and judge models while ensuring services meet standards for reliability, latency, and safety. Technical requirements include proficiency in C#, Python, and Azure services like Azure OpenAI and Cosmos DB. The work involves implementing secure AI patterns, managing CI/CD pipelines, and integrating with various data sources to ground agent responses in trusted signals while solving complex customer support and automation problems.
Develop scalable agentic AI solutions using LLM orchestration, RAG pipelines, and tool integrations.
Write, review, and maintain production-quality C# and Python code for reusable components and engineering patterns.
Translate customer requirements into technical designs and manage the full lifecycle from implementation to deployment.
Build evaluation frameworks using rubrics, golden datasets, and judge models to guide agents toward production.
Implement secure AI patterns including prompt-injection defenses, least-privilege tool access, and data boundary enforcement.
Integrate agent responses with Azure data sources, APIs, and cloud-native services to ensure grounded actions.
Operate production services using CI/CD, automated testing, feature flags, tracing, and incident response.
Lead design reviews and provide technical mentorship to other engineers on applied AI and production software.
What we're looking for
Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience in languages like C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Experience building or integrating with LLM-based systems including prompt engineering, RAG, or agent frameworks.
Experience building and shipping agentic AI services or multi-agent workflows in production.
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, and data-boundary enforcement.
Experience with CI/CD pipelines, automated testing, production observability, infrastructure as code, container orchestration, and staged production deployment.
Background in customer support engineering, supportability, diagnostics, or customer-experience platforms (preferred).
LLM Orchestration
RAG
Python
C#
Azure
Microservices
CI/CD
Infrastructure as Code
Containers
APIs
Distributed Systems
Prompt Engineering
Observability
Machine Learning
Agentic AI
C++
Java
JavaScript
C
System Design
Python
LLM
Retrieval-Augmented Generation
Agentic Workflows
Machine Learning
AI Systems
Git
Production Monitoring
Observability
Model Evaluation
Backend Services
Python
Large Language Models (LLMs)
Agentic AI
RDF
OWL
SPARQL
Knowledge Graphs
Microsoft Fabric
Microsoft Azure
LangChain
Spark
SQL
ETL
ELT
CI/CD
MLOps
LLMOps
Microservices
Go
AI
ML
SaaS
RESTful APIs
Microservices
SQL Server
PostgreSQL
Kubernetes
Docker
Terraform
CI/CD
Linux
MLOps
Vector Databases
Distributed Systems
Message Queues
Event-driven Architecture
Infrastructure Automation
Observability