New 
Software Engineer 2
 Microsoft | |
 United States, Washington, Redmond | |
 Jul 05, 2025 | |
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OverviewThe 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 Solutions (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 welcome a new challenge each day. In doing so, we create life-changing innovations that impact billions of lives around the world. Within Learn 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 work on a strategic initative at Microsoft. The goal of LPE is to build the next generation of Skilling platforms and applications running on Microsoft stack like Dynamics 365, Artificial 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, rapidly-changing 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 a Software Engineer 2 to join our Agentic AI Workforce team. We are looking for somone with deep expertise in machine learning, long language model (LLM) integration, and agent orchestration. We are seeking a candidate who 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.
ResponsibilitiesML & AI Development Contribute to the design and development of machine learning models and intelligent agents, optimizing for performance and efficiency. Apply cutting-edge techniques 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. Optimize models 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 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 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 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 Develop, optimize 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. Support the AI roadmap by contributing to the evaluations of emerging technologies and advocating for scalable, impactful solutions. Research, Innovation & AI Thought Leadership Stay current with advancements in AI/ML, including LLMs, multimodal learning, and agentic frameworks. Contribute to 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 and contribute to team knowledge sharing. OtherEmbody ourCultureandValues. | |