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Dynamics 365 is Microsoft’s suite of enterprise software that powers many of the largest businesses in the world. The Customer Experience Applications Team delivers Dynamics 365 Contact Center, an AI-first solution that lets our customers run intelligent and highly scalable contact centers. We are building the next generation of our applications running on Azure that pull together Dynamics 365, Office 365 and a number of other Microsoft cloud services to deliver high value, complete, and predictive application scenarios across all devices and form factors. D365 Contact Center is a robust application that extends the power of CRM’s like Dynamics 365 Customer Service to enable organizations to instantly connect and engage with their customers via channels like Live Chat, Voice, and SMS. As an Software Engineer II - AI in the Microsoft Dynamics Customer Experience Applications team, you will apply both software engineering and AI expertise to design and implement intelligent solutions within Dynamics 365. You’ll collaborate with business and technology leaders, internal users, and partners to build scalable, production-ready systems that leverage AI to solve complex business requests. In this role, you are expected to bring software engineering fundamentals—architecture, coding, testing, and deployment—while also selecting and integrating the most effective AI models and frameworks to deliver measurable impact and innovation. We innovate quickly and collaborate closely with our partners and customers in a very agile environment. If the opportunity to collaborate with a diverse engineering team, on enabling end-to-end business scenarios using cutting-edge AI first technologies and to solve problems for large scale 24x7 business SaaS applications excite you, we would love to talk to you! 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.
Job Responsibility:
Design and develop highly usable, scalable application capabilities, integrating AI models and enhancing existing features to meet evolving customer needs
Build and debug production-grade code in distributed systems
Translate business requirements into AI solutions, collaborating with data scientists, product managers, and engineering teams to ensure alignment and impact
Optimize AI model performance and reliability in production environments, including retraining, evaluation, and continuous monitoring
Own deployment, quality and operation of AI systems, including automated testing, CI/CD pipelines, deployment, and monitoring with MLOps and DevOps practices
Troubleshoot live site issues as part of both product development and live site support rotations, ensuring rapid resolution and learning
Ensure high reliability and performance of applications and services through intelligent monitoring, alerting, and proactive failover strategies
Requirements:
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
Ability to meet Microsoft, customer and/or government security screening requirements
This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter
Nice to have:
Master's Degree in Computer Science or related technical field AND 3+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
OR Bachelor's Degree in Computer Science or related technical field AND 5+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
OR equivalent experience
AI & Domain Experience: Deep expertise in one or more AI domains, with a proven track record of deploying and scaling AI models in cloud environments
MLOps & LLMOps: experience with MLOps workflows (CI/CD, monitoring, retraining pipelines) and familiarity with modern LLMOps frameworks
Cloud & Infrastructure: Skilled in building and operating infrastructure using Azure, AWS, or Google Cloud, and deploying containerized models with Docker, Kubernetes, or similar tools