Senior Software Engineer - Responsible AI - CoreAI
Listed on Microsoft’s official careers site. Posted October 12, 2026. Check that it is still open and apply there.
Apply on Microsoft’s careers site →Job description
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. 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. 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 These requirements include, but are not limited to the following specialized security screenings: 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
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