Software Development Engineer II - Applied AI

Microsoft Corporation•Redmond, WA

31d

About The Position

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 ofâ¯CRM's like Dynamics 365 Customer Serviceâ¯to enable organizations to instantly connect and engage with their customers via channels like Live Chat, Voice, and SMS. As a Software Development Engineer II - Applied 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. 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.

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.
1+ years of professional experience working with generative artificial intelligence, large language models, or agent-based systems.
Ability to meet Microsoft customer and/or government security screening requirements are required for this role. These requirements include but are not limited to the following specialized security screenings: Microsoft Cloud Background Check: This position will be required to pass the Microsoft Cloud Background Check upon hire/transfer and every two years thereafter.

Nice To Haves

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 Expertise: Deep expertise in one or more AI domains, with a proven track record of deploying and scaling AI models in cloud environments.
MLOps & LLMOps: Strong 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.
Experience with customers success, zero trust security and compliance

Responsibilities

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 strong 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.

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