Software Engineer

Microsoft is a company where passionate innovators come to collaborate, envision what can be and take their careers further. This is a world of more possibilities, more innovation, more openness, and the sky is the limit of thinking in a cloud-enabled world. 

The mission of Learning Product Team in Worldwide Learning (WWL) - is to develop world-class, innovative Skilling Products & Experiences that inspire customers, partners, Microsoft Customer and Partner Soultions (MCAPS) sellers, and future generations to achieve more by skilling, upskilling, and reskilling, thereby reaching 100M+ learners. Our culture is cantered on embracing a growth mindset, a theme of inspiring excellence, and encouraging teams and leaders to drive impact each day. In doing so, we create life-changing innovations that impact billions of lives around the world. 

Within Learning Product Engineering Team, we develop enterprise-grade platforms and features impacting millions of learners around the world who rely on Microsoft Skilling platforms for consuming the learning and skilling content across various across platforms. 

This is an exciting time to join our group,Worldwide Learning Product Engineering (LPE),and be aligned to strategic to Microsoft as a Software Engineer. The goal of LPE is to build the next generation of Skilling platforms and applications running on Microsoft stack like Dynamics 365, Artifical Intelligence (AI), Copilot, and several other Microsoft cloud services to deliver value, complete, and Copilot-enabled application scenarios across all devices and form factors for our Commercial, Consumer and NextGen Learners. We innovate quickly and collaborate closely with our partners and customers in an agile, energetic environment. Leveraging the scalability and value from Azure, we ensure our solutions are robust and efficient. For instance, we will need to modernize Skilling content operations and reimagine release & update at scale to enable more efficient ways of consumption across Learn, YouTube, LinkedIn, Instagram and various other learning channels which will require deeper AI and Machine Learning (ML) skills. 

The Agentic AI Workforce team is a specialized group within the LPE organization, dedicated to designing, deploying, and maintaining AI agents that enhance both learning platform experiences and business planning operations. This team will serve as a central hub for innovation in agentic automation, enabling scalable, intelligent support across a range of enterprise scenarios. By integrating advanced AI capabilities into learning journeys and planning workflows, the team will empower users with personalized, context-aware assistance while driving operational efficiency and strategic insight across the organization. 

We are seeking an AI & ML Software Engineer to join our Agentic AI Workforce team. This Software Engineer - Artificial Intelligence (AI) is someone with knowledge in Long-Language Model (LLM) integration, agent orchestration and supporting Azure services. They will lead the development of intelligent agents that power adaptive learning experiences and support business planning workflows. They will be responsible for designing scalable AI pipelines, integrating with enterprise data sources, and ensuring robust performance and security. This role also involves close collaboration with cross-functional teams and mentoring peers to drive innovation in agentic automation. 

If the opportunity to collaborate with a diverse engineering team, on enabling end-to-end business scenarios usingcutting-edgetechnologies and to solve problems for large scale enterprise business scenarios excite you, please come and talk to us! 

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. 

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 ofLLMs, Retrieval Augmented Generation (RAG) pipelines and agent orchestration frameworks.
• 2+ years experience within programming & development: proficiencyinPythonand 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 inAzure 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:Experiencein handling large-scale structured and unstructured datasets, including time-series and text data, and applying advancedfeature engineering techniques. 
• 3+ years experience cross-functional collaboration: Ability topartner with software engineers, product managers, and business stakeholdersto 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.

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 $131,400 - $215,400 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.

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 $158,400 - $258,000 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   

Microsoft will accept applications and processes offers for these roles on an ongoing basis.

Microsoft is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, family or medical care leave, gender identity or expression, genetic information, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran status, race, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances.  We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you need assistance and/or a reasonable accommodation due to a disability during the application or the recruiting process, please send a request via the Accommodation request form.

Benefits/perks listed below may vary depending on the nature of your employment with Microsoft and the country where you work.

• 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 ourCultureandValues.