Join Microsoft's CoreAI organization, a driving force behind Azure AI innovation and differentiation across flagship products like GitHub, Office, Teams, and Xbox. This team builds end-to-end AI solutions, including Responsible AI, Azure OpenAI, Model as a Service, Azure ML, Cognitive Services, and the global Azure AI infrastructure for massive AI workloads.

The Responsible AI group specifically addresses risks associated with AI-generated and human-generated content across text, image, audio, video, and multimodal formats. We are seeking a passionate Software Engineer to develop customer-facing AI services that are high-performance, low-latency, and highly available.

In this pivotal role, you will collaborate with accomplished engineers, scientists, and product managers to shape industry-leading Responsible AI services. You will be instrumental in creating new AI services and integrating them with existing platforms such as Azure AI Content Safety, Azure OpenAI Service, and Azure AI Studio.

Your responsibilities will encompass leading AI-native development, enhancing AI tools and practices throughout the software development lifecycle, and ensuring the quality of AI-generated artifacts. You will embed Responsible AI controls and engineering health metrics, driving best practices for secure, performant, and maintainable code using modern patterns, including Generative AI where applicable.

You will own architecture and design decisions for complex systems, meticulously evaluating trade-offs for security, privacy, scalability, resilience, cost, and testability, while managing cross-team dependencies. Additionally, you will drive engineering excellence by promoting best practices, continuous learning, and developer tooling, with a particular emphasis on security, compliance evidence, automation, and scalable delivery.

Execution will involve planning and implementing deliveries with experimentation, clear success metrics, secure rollout strategies, and automated deployments. You will also build and enhance observability and on-call readiness, lead incident response and postmortems, and reduce incident volume through systemic fixes. Collaborating with stakeholders, you will validate user requirements, capture needs (including security and privacy), and define feedback loops for continuous improvement.

A Bachelor's degree in Computer Science, Engineering, or a related technical field, coupled with equivalent practical experience, is required. You should possess at least 12 years of professional software engineering experience, including hands-on development in modern programming languages such as C#, Java, or Python.

A strong foundation in core software engineering principles, including data structures, algorithms, object-oriented design, and scalable system principles, is essential. Experience in developing, testing, debugging, and maintaining production-quality code, adhering to established coding standards and development processes, is also a must.

Working knowledge of cloud, distributed systems, or service-based architectures, with a solid understanding of reliability, performance, and monitoring concepts, is necessary. The role demands expertise in designing and architecting business solutions, employing design principles across diverse technological landscapes, and guiding projects through the engineering lifecycle to align with business goals and ensure customer satisfaction.

Preferred qualifications include 14+ years of hands-on software engineering experience, proficiency in languages like C# or Python, and a deep understanding of production-quality software development. Strong technical judgment and problem-solving skills, including the ability to design and implement scoped solutions, debug complex issues, refactor code, and enhance service reliability and performance, are highly valued. The capacity to collaborate effectively, influence peers, navigate team dynamics, and contribute positively to team engineering culture is also crucial.

Demonstrated collaboration and emerging leadership skills, such as mentoring engineers, owning components or features, and supporting operational excellence, are beneficial. Exposure to AI platforms, large language models (LLMs), and core machine-learning concepts, with a genuine interest or early experience in applying them to real-world software engineering challenges, would be an advantage. Candidates must also meet Microsoft, customer, and government security screening requirements, including a Microsoft Cloud Background Check.