Overview
As a Software Engineer in the Microsoft Dynamics Customer Experience Applications team, you will contribute to the design and implementation of intelligent solutions within Dynamics 365 by applying both software engineering and AI skills. You’ll work closely with senior engineers, business stakeholders, and partners to help build scalable, production-ready systems that leverage AI to address real-world business challenges.
This role is expected to demonstrate strong software engineering fundamentals—including coding, testing, and deployment—while integrating and optimizing AI models and frameworks. The position contributes to delivering reliable, impactful, and innovative solutions, with mentorship and guidance from more senior team members.
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.
Responsibilities
As a Software Engineer with an applied AI focus on the Microsoft Dynamics Customer Experience Applications team, this role applies both software engineering and AI expertise to build intelligent, scalable solutions that power Dynamics 365 services used globally. Collaboration with cross‑functional teams supports the delivery of high‑impact features aligned with enterprise standards and cloud‑scale requirements. Responsibilities include:
- Developing highly usable, scalable application capabilities, integrating AI models and enhancing existing features to meet evolving customer needs.
- Building and debuging production-grade code in distributed systems
- Translating business requirements into AI solutions, collaborating with data scientists, product managers, and engineering teams to ensure alignment and impact.
- Optimizing AI model performance and reliability in production environments, including retraining, evaluation, and continuous monitoring.
- Owning deployment, quality and operation of AI systems, including automated testing, CI/CD pipelines, deployment, and monitoring with strong MLOps and DevOps practices.
- Troubleshooting live site issues as part of both product development and live site support rotations, ensuring rapid resolution and learning.
- Ensuring high reliability and performance of applications and services through intelligent monitoring, alerting, and proactive failover strategies.
Qualifications
Required Qualifications
- Bachelor's Degree in Computer Science, or related technical discipline with proven experience coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience.
Other Requirements:
- 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.
Preferred Qualifications:
- Experience with customers success, zero trust security and compliance.
- Experience with coding in languages including, but not limited to, Python, C#, Java, Rust, or C++.
- Experience with GenAI, LLMs, or agentic systems.
- Advanced degree (MS or PhD) in Computer Science, Machine Learning, or a related field.
- Deep expertise in one or more AI domains, with a proven track record of deploying and scaling AI models in cloud environments.
- Strong experience with MLOps workflows (CI/CD, monitoring, retraining pipelines) and familiarity with modern LLMOps frameworks.
- Skilled in building and operating infrastructure using Azure, AWS, or Google Cloud, and deploying containerized models with Docker, Kubernetes, or similar tools.
