Microsoft Software Engineer 

Taiwan, Taoyuan City 

81043415

The mission ofOur culture is

Learn product Engineeringdevelop enterprise-gradeacross platforms

and work on a strategic initative atis to build the next generation ofapplications running onfor our Commercial, Consumer and NextGen LearnersAzure, we ensure our solutions are robust and efficient.For instance, we will need to modernizecontent operations and reimagine release& updateat scale to enable more efficient ways of consumption across Learn, YouTube, LinkedIn,and various other learning channels which will require deeper AI and machine learning (ML) skills.

organization, dedicated to designing, deploying, andAI agents that enhance both learning platformwhile driving operational efficiency and strategic insight across the organization.

be responsible for

technologies and to solve problems for large scale

Required/minimum qualifications

• 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 OR equivalent experience.
• 1+ years experience working in AI, ML, LLMs or an emerging technology or industry disrupting technology.

Certain roles may be eligible for benefits and other compensation. Find additional benefits and pay information here:

ML & AI Development

• designand development of machine learning models and intelligent agents,optimizing forperformance and efficiency.
• Applycutting-edgetechniques in LLMs, reinforcement learning, and agent orchestration to enable autonomous, context-aware AI behaviors.

Scalable Model Deployment & Optimization

• Build and deploy ML models and agentic systems in cloud environments (preferably Azure), ensuring seamless integration with enterprise platforms and services.
• Optimizemodels for inference speed and resource efficiency using techniques such as quantization, pruning, distillation, and hardware acceleration (e.g., graphics processing unit (GPUs), tensor processing unit (TPUs)).
• Implement A/B testing, model evaluation, and hyperparameter tuning pipelines to drive continuous performance improvement.

ML Architecture & Automation

• Develop automated pipelines for data ingestion, preprocessing, feature engineering, model training, and deployment with an emphasis on reproducibility and traceability.
• Enable continuous learning and experimentation through efficient retraining, model versioning, and deployment automation.

Agent Protocols,Governance & Compliance

• mplement multi-agent communication protocols (e.g., MCP) to support coordination, task delegation, and stateful interactions between AI agents.
• Ensure all AI systems adhere to responsible AI principles, including fairness, transparency, and privacy-preserving practices.
• Establish monitoring and governance frameworks for model drift detection, performance tracking, and secure deployment.

Agent Lifecycle Management

• mplement lifecycle management strategies for AI agents, including provisioning, monitoring, updating, and decommissioning.
• Establish observability practices for agent behavior, including logging, tracing, and performance metrics.

Human-AI Interaction & UX Alignment

• with user experience (UX) designers and product teams to ensure AI agent interactions are intuitive, transparent, and aligned with user expectations.
• Contribute to the design of feedback loops that allow users to correct or guide agent behavior, improving learning and trust over time.

Knowledge Management & Retrieval

• ,optimizeandmaintainretrieval-augmented generation (RAG) pipelines that allow agents to access and reason over enterprise knowledge bases.
• Implement vector search, embedding strategies, and document chunking techniques tooptimizeinformation retrieval for agentic tasks.

Cross-Functional Collaboration & AI Strategy

• Collaborate with full stack and Power Platform engineers to integrate AI agents into learning platforms and business planning tools.
• Partner with product managers and business stakeholders to align AI initiatives with strategic goals and user needs.
• Supportthe AI roadmap bycontributing tothe evaluationsofemerging

Research, Innovation & AI Thought Leadership

• Stay current with advancements in AI/ML, including LLMs, multimodal learning, and agentic frameworks.
• Contribute toproof-of-concept initiatives to evaluatenew technologiesand assess their applicability to enterprise use cases.
• Contribute to the broader AI community through publications, conference participation, and open-source contributions.

Mentorship & AI Talent Development

• Mentor earlier in career and mid-level engineers

• Embody ourand.