Senior Software Engineer

Microsoft Security (MSEC) is seeking a Senior AI Engineer to lead the development ofAI-native, multi-agent systems that help customers securely adopt AI at an enterprise scale.  

This role sits at the intersection ofAI engineering, security, and customer readiness, bridging the gap between cutting-edge AI capabilities (LLMs, agentic systems) andreal-world enterprise adoption. You will design, build, and deploy intelligent systems that transform complex signals across identity, devices, data, applications, and infrastructure intoactionable intelligence, automation, and measurable outcomes.  

You will operate in a highly collaborative, cross-company environment, driving end-to-end execution—fromAI model development and data pipelines to production deployment, telemetry, and continuous optimization—while shaping how enterprises prepare for and securely adopt AI.  

As an AI Engineer, you will bridge the gap between AI research and real-world applications, enabling automation, enhanced decision-making, reasoning, and innovation. 

Responsibilities (Enhanced with Modern AI Engineering Expectations) 

AI Systems, Models & Platform Engineering 

• Design and buildmulti-agent AI systems leveraging LLMs, RAG pipelines, and vector-based retrieval systems to operationalize customer readiness across security domains. 

• Develop and productionizemachine learning and deep learning models that transform large-scale, multi-source enterprise signals into contextual intelligence and automation. 

• Architect scalable systems fordata ingestion, feature engineering, and model training, integrating signals across Microsoft services.  

• Implementoptimization and automation algorithms for prediction, prioritization, and decision-making across AI readiness workflows. 

Data, MLOps & Productionization 

• Build and operatescalable data pipelines, ETL workflows, and training infrastructure to support AI lifecycle management. 

• Deploy models into production usingMLOps practices (CI/CD, model versioning, containerization) to ensure reliability, reproducibility, and scalability. 

• Monitor deployed AI systems forperformance, drift, reliability, and security risks, continuously improving through telemetry and feedback loops. 

• Establish best practices formodel governance, evaluation, and lifecycle management aligned with enterprise security and compliance requirements. 

AI Readiness & Customer Impact 

• Define and operationalizeAI readiness frameworks, metrics, and telemetry to measure adoption maturity and security posture.  

• Translate customer scenarios intodeployable AI solutions, playbooks, and onboarding frameworks that enable secure AI adoption at scale. 

• Embed AI into customer workflows viaAPIs, services, and platform integrations, delivering end-to-end experiences. 

Builder Mindset & Iteration Velocity 

• Demonstrate astrong builder mindset with a bias for action—rapidly prototyping and iterating on AI solutions, evolving them from experimentation to production-scale systems.  

• Operate in ambiguous environments, converting problem spaces intoworking AI systems using iterative development, experimentation, and telemetry-driven refinement. 

Cross-Company Collaboration & Integration 

• Partner across Engineering, Data Science, Product, and Customer Experience teams totranslate business problems into AI-driven solutions.  

• Drive integration of AI capabilities intoproducts, services, and APIs, ensuring seamless end-to-end customer experiences. 

• Align stakeholders across a matrixed organization to delivercohesive, platform-level solutions at enterprise scale. 

Technical Leadership & Operational Excellence 

• Lead end-to-end delivery ofcomplex AI and security initiatives, from architecture through production readiness and operational scale.  

• Build telemetry, instrumentation, and analytics totrack adoption, system performance, and business impact.  

• Drivedata-informed decision-making, converting system signals into actionable insights and continuous improvements. 

• Establish governance, documentation, and engineering standards to ensuremaintainability, transparency, and reproducibility of AI systems. 

Qualifications

Required 

• Bachelor’s Degree AND 4+ years of experience in AI engineering, system design, or data engineering.  

• Hands-on experience designing and deployingproduction-grade AI/ML systems, including LLM-based or agentic systems. 

• Strong programming skills (e.g., Python) formodel development, data pipelines, and system integration. 

• Experience building and operatingdistributed systems and scalable data/ML pipelines. 

Preferred

• 8+ years of experience in AI/ML engineering and large-scale distributed systems.  

• Deep experience withLLMs, RAG architectures, vector databases, and agentic workflows. 

• Expertise inMLOps (CI/CD for ML, model monitoring, versioning, containerization) and production deployment. 

• Strong understanding ofstatistics, optimization, and machine learning fundamentals. 

• Experience buildingenterprise-grade AI systems on cloud platforms (Azure preferred). 

• Proven ability to operate inambiguous, cross-org environments and deliver end-to-end systems. 

• Strong communication skills totranslate complex AI systems into clear business and executive insights.  

• Demonstrated leadership inAI adoption, platform building, or security domains. 

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year. There is a different range applicable to specific work locations, within the San Francisco Bay area and New York City metropolitan area, and the base pay range for this role in those locations is USD $160,200 - $261,000 per year.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:
https://careers.microsoft.com/us/en/us-corporate-pay

This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance with religious accommodations and/or a reasonable accommodation due to a disability during the application process, read more aboutrequesting accommodations.