Join a dynamic global team focused on enhancing cloud operational excellence through AI-powered automation. The Engineering Operations (EngOps) organization is dedicated to strengthening quality, reliability, security, and customer trust. We proactively prevent issues by transforming operational signals into actionable insights, achieving measurable customer impact at cloud scale. Our culture champions empowerment, inclusion, and a growth mindset.

Within EngOps ACES, the Agent Fleet Operations (AFO) team seeks an AI-first software engineer for the Cloud Operational Demand Engineering (CØDE) organization. Advance our mission to drive operational demand to zero by leveraging a DevOps background—including CI/CD, release engineering, safe deployments, observability, and production operations—combined with AI engineering expertise and a passion for continuous learning.

Develop and deploy production AI solutions to minimize operational demand and elevate customer outcomes. You will be instrumental in transforming unresolved customer issues into autonomously resolved, satisfying resolutions. Additionally, you will contribute to building robust release, observability, and reliability practices essential for safely operating a fleet of autonomous agents within the customer journey.

As an individual contributor, you will actively build and refine agentic patterns that reduce operational demand and enhance customer satisfaction. This role demands ownership of delivery, quality, and production health for your developed components, extending beyond mere design. Ideal candidates will have a proven track record in building and shipping production-scale AI systems, coupled with expertise in CI/CD, safe deployment, and reliability practices, eager to dive deep into agentic resolution while fostering cross-team collaboration.

Qualifications include a Bachelor's Degree in Computer Science, Engineering, or a related field, coupled with over 8 years of technical engineering experience in software engineering, applied AI/ML, or distributed systems, or equivalent experience. A strong background includes at least 8 years of IT experience and 3+ years specifically using Agentic AI. Demonstrated hands-on experience in building and deploying AI or machine learning systems in production is essential.

Crucially, candidates must possess a solid DevOps or SRE foundation. This involves practical experience with CI/CD and release engineering, safe deployment methodologies (e.g., blue/green, canary, progressive rollout, automated rollback), observability and monitoring tools, and active participation in production incident response and on-call rotations for always-on services.

Preferred qualifications encompass a Master's degree in Computer Science, AI, or a related field, and experience with multi-agent orchestration systems or agentic AI services in production. Familiarity with AI evaluation frameworks, Azure AI platforms, and agent security patterns is advantageous. Depth in the DevOps/SRE toolchain, including infrastructure-as-code and progressive-delivery tooling, is highly valued. Experience with observability engineering, reliability practices, and contributing to cross-organizational engineering delivery will further strengthen an application.