AI Engineer II & Senior AI Engineer - Getting Customers Ready for AI

Microsoft Microsoft · Big Tech · Redmond, WA +1 · Software Engineering

AI Engineer role focused on designing, building, and deploying AI-native systems for enterprise customers to securely adopt AI at scale. This involves working across the AI lifecycle, including model development, data pipelines, deployment, monitoring, and MLOps, with a focus on LLM-based applications, RAG, and agentic workflows within a security context.

What you'd actually do

  1. Design, build, and improve AI-powered solutions, including machine learning models, LLM-based applications, RAG systems, and agentic workflows that solve customer and business challenges.
  2. Develop and operate scalable data pipelines, ETL processes, training datasets, and AI infrastructure to support model development, deployment, and lifecycle management.
  3. Deploy, monitor, and optimize AI systems using MLOps practices, leveraging telemetry and feedback to improve performance, reliability, security, and scalability.
  4. Transform large-scale, multi-source data into contextual intelligence, automation, and decision-support capabilities that drive customer and business outcomes.
  5. Build and integrate AI capabilities into applications, services, APIs, and platforms to deliver end-to-end customer experiences.

Skills

Required

  • Bachelor's Degree in Computer Science or related technical field AND 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
  • Ability to meet Microsoft, customer and/or government security screening requirements

Nice to have

  • Experience building, deploying, or contributing to AI/ML systems, including machine learning models, LLM-based applications, RAG architectures, agentic workflows, or other data-driven solutions.
  • Demonstrated programming skills in Python or similar languages for AI development, data processing, system integration, and automation.
  • Knowledge of machine learning fundamentals, statistics, optimization, and data processing techniques.
  • Experience developing and operating scalable data pipelines, distributed systems, or cloud-based AI/ML workloads.
  • Familiarity with MLOps practices such as CI/CD, model deployment, monitoring, versioning, containerization, and AI lifecycle management.
  • Experience working with modern AI technologies, including LLMs, vector databases, retrieval systems, and related AI frameworks and tools.
  • Problem-solving skills, curiosity, and the ability to learn quickly, navigate ambiguity, and deliver solutions in rapidly evolving technology environments.

What the JD emphasized

  • AI-native systems
  • securely adopt AI
  • enterprise scale
  • AI lifecycle
  • MLOps practices
  • customer and business challenges
  • secure and scalable AI solutions
  • securely adopt and operationalize AI technologies
  • Responsible AI practices

Other signals

  • AI-native systems for enterprise scale
  • securely adopt AI
  • AI lifecycle
  • MLOps practices
  • customer and business challenges