Job Description

Locations

• Redmond, WA, US

Salary

Skills Required

• AI engineeringintermediate
• and operating distributed systemsintermediate
• AI/ML engineeringintermediate
• LLMsintermediate
• MLOpsintermediate
• enterprise-grade AI systems on cloud platformsintermediate

Required Qualifications

• Bachelor’s Degree AND 4+ years of experience in AI engineering, system design, or data engineering. (experience, 4 years)
• Bachelor’s Degree AND 4+ years of experience in AI engineering, system design, or data engineering. (experience, 4 years)
• Hands-on experience designing and deploying production-grade AI/ML systems, including LLM-based or agentic systems. (experience)
• Hands-on experience designing and deploying production-grade AI/ML systems, including LLM-based or agentic systems. (experience)
• Strong programming skills (e.g., Python) for model development, data pipelines, and system integration. (experience)
• Strong programming skills (e.g., Python) for model development, data pipelines, and system integration. (experience)
• Experience building and operating distributed systems and scalable data/ML pipelines. (experience)
• Experience building and operating distributed systems and scalable data/ML pipelines. (experience)

Preferred Qualifications

• 8+ years of experience in AI/ML engineering and large-scale distributed systems. (experience, 8 years)
• 8+ years of experience in AI/ML engineering and large-scale distributed systems. (experience, 8 years)
• Deep experience with LLMs, RAG architectures, vector databases, and agentic workflows. (experience)
• Deep experience with LLMs, RAG architectures, vector databases, and agentic workflows. (experience)
• Expertise in MLOps (CI/CD for ML, model monitoring, versioning, containerization) and production deployment. (experience)
• Expertise in MLOps (CI/CD for ML, model monitoring, versioning, containerization) and production deployment. (experience)
• Strong understanding of statistics, optimization, and machine learning fundamentals. (experience)
• Strong understanding of statistics, optimization, and machine learning fundamentals. (experience)
• Experience building enterprise-grade AI systems on cloud platforms (Azure preferred). (experience)
• Experience building enterprise-grade AI systems on cloud platforms (Azure preferred). (experience)
• Proven ability to operate in ambiguous, cross-org environments and deliver end-to-end systems. (experience)
• Proven ability to operate in ambiguous, cross-org environments and deliver end-to-end systems. (experience)
• Strong communication skills to translate complex AI systems into clear business and executive insights. (experience)
• Strong communication skills to translate complex AI systems into clear business and executive insights. (experience)
• Demonstrated leadership in AI adoption, platform building, or security domains. (experience)
• Demonstrated leadership in AI adoption, platform building, or security domains. (experience)
• 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. (experience)
• 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 (experience)
• This position will be open for a minimum of 5 days, with applications accepted on an ongoing basis until the position is filled. (experience)

Responsibilities

• Responsibilities (Enhanced with Modern AI Engineering Expectations)
• AI Systems, Models & Platform Engineering
• Design and build multi-agent AI systems leveraging LLMs, RAG pipelines, and vector-based retrieval systems to operationalize customer readiness across security domains.
• Design and build multi-agent AI systems leveraging LLMs, RAG pipelines, and vector-based retrieval systems to operationalize customer readiness across security domains.
• Develop and productionize machine learning and deep learning models that transform large-scale, multi-source enterprise signals into contextual intelligence and automation.
• Develop and productionize machine learning and deep learning models that transform large-scale, multi-source enterprise signals into contextual intelligence and automation.
• Architect scalable systems for data ingestion, feature engineering, and model training, integrating signals across Microsoft services.
• Architect scalable systems for data ingestion, feature engineering, and model training, integrating signals across Microsoft services.
• Implement optimization and automation algorithms for prediction, prioritization, and decision-making across AI readiness workflows.
• Implement optimization and automation algorithms for prediction, prioritization, and decision-making across AI readiness workflows.
• Data, MLOps & Productionization
• Build and operate scalable data pipelines, ETL workflows, and training infrastructure to support AI lifecycle management.
• Build and operate scalable data pipelines, ETL workflows, and training infrastructure to support AI lifecycle management.
• Deploy models into production using MLOps practices (CI/CD, model versioning, containerization) to ensure reliability, reproducibility, and scalability.
• Deploy models into production using MLOps practices (CI/CD, model versioning, containerization) to ensure reliability, reproducibility, and scalability.
• Monitor deployed AI systems for performance, drift, reliability, and security risks, continuously improving through telemetry and feedback loops.
• Monitor deployed AI systems for performance, drift, reliability, and security risks, continuously improving through telemetry and feedback loops.
• Establish best practices for model governance, evaluation, and lifecycle management aligned with enterprise security and compliance requirements.
• Establish best practices for model governance, evaluation, and lifecycle management aligned with enterprise security and compliance requirements.
• AI Readiness & Customer Impact
• Define and operationalize AI readiness frameworks, metrics, and telemetry to measure adoption maturity and security posture.
• Define and operationalize AI readiness frameworks, metrics, and telemetry to measure adoption maturity and security posture.
• Translate customer scenarios into deployable AI solutions, playbooks, and onboarding frameworks that enable secure AI adoption at scale.
• Translate customer scenarios into deployable AI solutions, playbooks, and onboarding frameworks that enable secure AI adoption at scale.
• Embed AI into customer workflows via APIs, services, and platform integrations, delivering end-to-end experiences.
• Embed AI into customer workflows via APIs, services, and platform integrations, delivering end-to-end experiences.
• Builder Mindset & Iteration Velocity
• Demonstrate a strong builder mindset with a bias for action—rapidly prototyping and iterating on AI solutions, evolving them from experimentation to production-scale systems.
• Demonstrate a strong 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 into working AI systems using iterative development, experimentation, and telemetry-driven refinement.
• Operate in ambiguous environments, converting problem spaces into working AI systems using iterative development, experimentation, and telemetry-driven refinement.
• Cross-Company Collaboration & Integration
• Partner across Engineering, Data Science, Product, and Customer Experience teams to translate business problems into AI-driven solutions.
• Partner across Engineering, Data Science, Product, and Customer Experience teams to translate business problems into AI-driven solutions.
• Drive integration of AI capabilities into products, services, and APIs, ensuring seamless end-to-end customer experiences.
• Drive integration of AI capabilities into products, services, and APIs, ensuring seamless end-to-end customer experiences.
• Align stakeholders across a matrixed organization to deliver cohesive, platform-level solutions at enterprise scale.
• Align stakeholders across a matrixed organization to deliver cohesive, platform-level solutions at enterprise scale.
• Technical Leadership & Operational Excellence
• Lead end-to-end delivery of complex AI and security initiatives, from architecture through production readiness and operational scale.
• Lead end-to-end delivery of complex AI and security initiatives, from architecture through production readiness and operational scale.
• Build telemetry, instrumentation, and analytics to track adoption, system performance, and business impact.
• Build telemetry, instrumentation, and analytics to track adoption, system performance, and business impact.
• Drive data-informed decision-making, converting system signals into actionable insights and continuous improvements.
• Drive data-informed decision-making, converting system signals into actionable insights and continuous improvements.
• Establish governance, documentation, and engineering standards to ensure maintainability, transparency, and reproducibility of AI systems.
• Establish governance, documentation, and engineering standards to ensure maintainability, transparency, and reproducibility of AI systems.

Benefits

• general: Flexibility: Balance what matters—your work, your life, and your team—through trust, autonomy, and shared accountability
• general: Growth: Stretch your skills, expand your impact, and grow with support that meets you where you are
• general: Wellbeing: Support for your body, mind, and financial future—so you can stay energized and do your best work
• general: Community PCS: Find your people, build your network, and feel supported every step of the way

Travel Requirements

Less than 25%

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