Principal Applied AI Engineer

' Architect and build production agentic AI systems: Design and implement scalable AI agents and services, including LLM orchestration, multi-agent workflows, RAG pipelines, tool integrations, memory and state management, and human-in-the-loop experiences. Lead through hands-on engineering: Write, review, and maintain production-quality C# and Python code. Build critical components, reference implementations, prototypes, and reusable platform capabilities that accelerate delivery across the engineering organization. Own end-to-end technical delivery: Translate ambiguous customer support scenarios into clear technical requirements and take solutions from architecture and experimentation through implementation, deployment, operation, and continuous improvement. Define technical architecture and engineering standards: Establish architecture patterns, API and data contracts, code quality expectations, evaluation practices, deployment strategies, and observability standards for AI services. Drive technical consistency while allowing teams to evolve their solutions. Advance evaluation-driven AI development: Design and implement evaluation frameworks using rubrics, golden datasets, simulation, judge models, and offline and online evaluation. Define quality thresholds and graduation criteria for moving agents from experimentation through shadow mode and into autonomous operation. Own production quality and reliability: Ensure AI services meet demanding standards for correctness, availability, latency, scalability, cost, and safety. Diagnose complex production issues, lead technical incident response and root cause analysis, and engineer systemic improvements. Design for responsible and secure AI: Embed responsible AI principles, privacy protections, prompt-injection defenses, tool authorization, data boundaries, action controls, auditability, and fail-safe behavior into system architecture and implementation. Make consequential technical decisions: Evaluate models, frameworks, platforms, and architectural approaches using evidence from experiments and production telemetry. Balance solution quality with reliability, maintainability, latency, cost, and operational complexity. Influence product and platform roadmaps: Partner with product managers, researchers, platform engineers, and Azure service teams to shape technical strategy and platform capabilities. Translate customer support patterns and operational signals into durable engineering investments. Drive measurable customer and business impact: Instrument systems and use data to improve automation accuracy, case volume reduction, resolution time, engineering efficiency, and customer satisfaction. Connect architectural investments to measurable outcomes. Provide technical leadership across teams: Lead architecture reviews, resolve complex technical disagreements, and align engineers and partner organizations around shared designs. Communicate clearly with engineering teams, senior leaders, and non-technical stakeholders. Raise the engineering capability of the organization: Mentor engineers in system design, software engineering, applied AI, and evaluation-driven development. Create reusable guidance and help teams develop the judgment to determine where AI agents should and should not act autonomously. Bachelor's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python OR equivalent experience. 8+ years of professional software engineering experience designing, building, and operating production software systems. Strong proficiency in C# and/or Python, with demonstrated ability to design and implement production-quality services and distributed systems.