About the job
The Responsible AI group focuses on identifying, measuring, mitigating, and monitoring Responsible AI risks in AI-generated and human-generated content spanning text, image, audio, video, and multimodal content. We are looking for a Principal Software Engineer - Responsible AI who is passionate about building customer-facing AI services with scalable and sustainable architecture and implementation and with high performance, low latency, and high availability. In this role, you will work with a unique group of talented engineers, scientists, and product managers to build the industry's best Responsible AI services. You will own the design of new AI services and integration with existing services such as Azure AI Content Safety, Azure OpenAI Service, Azure AI Studio, and more.
Responsibilities
Design and develop large-scale distributed cloud services and solutions with a focus on high availability, scalability, robustness, and observability.
Lead project development across the organization and work with subject matter experts and stakeholders to drive development and release plans.
Take end-to-end responsibility for the development lifecycle and production readiness of the services you build and drive the team’s DevOps culture.
Drive and uphold the best practices of modern software engineering through code and design reviews and take effective service decisions based on data and telemetry.
Understand Microsoft businesses and collaborate with stakeholders towards cohesive, end-to-end experiences for Microsoft customers.
Embrace a growth mindset and stay up to date with the current and state-of-the-art technologies to improve customer experience and better serve the product’s business needs.
Qualifications
Minimum
Bachelor's Degree in Computer Science or related technical field AND 4+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
Preferred
4+ years of technical engineering experience designing and delivering highly available, large-scale cloud services and distributed systems.
2+ years of technical engineering experience with machine learning model development, release, and operations.
Demonstrate depth of knowledge and understanding of software architecture, design tradeoffs, and practices of mature DevOps culture.
Experience using appropriate artificial intelligence (AI) tools and practices across the software development lifecycle (SDLC) in a disciplined manner.
Experience in any one or more of the following areas: Safety and governance platforms for AI models and agents; Inference, routing, orchestration, and policy enforcement systems; Evaluation, red teaming, and monitoring infrastructure for AI systems; Deployment automation, CI/CD, and compliance tooling (e.g., zero-manual-effort deployments); Multimodal safety infrastructure (image, video, audio, provenance); Agent governance and control-plane capabilities