Microsoft Software Engineer - Artificial Intelligence 

Taiwan, Taoyuan City 

854646788

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 within AI knowledge. Specifically theoretical and practical knowledge of LLMs, Retrieval Augmented Generation (RAG) pipelines and agent orchestration frameworks.
• 2+ years experience within programming & development: proficiency in Python and command of related libraries and frameworks such as TensorFlow, PyTorch,, Scikit-Learn, Hugging Face Transformers, LangChain, and similar.
• 2+ years experience within model deployment & operations: experience deploying agents in Azure cloud environments, with familiarity in containerization, continuous integration/continuous development pipelines and model monitoring.

Preferred Qualifications

• Advanced Degree: in Machine Learning, AI or a related field.
• 1+ years experience within agent communication & protocols: Experience designing and implementing multi-agent systems using communication protocols such as MCP or similar. Ability to structure agent interactions, manage stateful dialogs, and coordinate task execution across distributed AI agents in enterprise environments.
• 3+ years experience within data handling & feature development: Experience in handling large-scale structured and unstructured datasets, including time-series and text data, and applying advanced feature engineering techniques.
• 3+ years experience cross-functional collaboration: Ability to partner with software engineers, product managers, and business stakeholders to translate business needs into AI-driven solutions.

Software Engineering IC3 - The typical base pay range for this role across the U.S. is USD $100,600 - $199,000 per year.

Software Engineering IC4 - The typical base pay range for this role across the U.S. is USD $119,800 - $234,700 per year.

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

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

• Architecture & Automation
• Design scalable agentic architectures that support real-time inference, batch processing, and hybrid workflows.
• 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
• Design and implement 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
• Define and implement 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
• Partner with 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
• Develop and maintain retrieval-augmented generation (RAG) pipelines that allow agents to access and reason over enterprise knowledge bases.
• Implement vector search, embedding strategies, and document chunking techniques to optimize information 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.
• Influence the AI roadmap by evaluating emerging technologies and advocating for scalable, impactful solutions.

• Research, Innovation & AI Thought Leadership
• Stay current with advancements in AI, including LLMs, multimodal learning, and agentic frameworks.
• Lead proof-of-concept initiatives to evaluate new technologies and 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, fostering a culture of innovation, experimentation, and continuous learning.
• Lead technical reviews, architecture discussions, and knowledge-sharing sessions to elevate team capabilities.
• Identify skill gaps and support internal learning initiatives to ensure the team remains at the forefront of AI innovation.

• Other
• Embody our and .