Software Engineering, CoreAI

Design, build, and operate production-grade generative AI and multimodal systems, with end-to-end ownership from concept through deployment and service operations. Contribute to technical design for core GenAI capabilities (e.g., retrieval-augmented generation, context and memory, orchestration) and make data-driven tradeoffs across quality, latency, cost, and safety. Define and improve model and system quality using evaluation frameworks, experiment design, and production telemetry; ensure robust testing and regression coverage. Collaborate with security, privacy, and compliance partners to build solutions that meet enterprise requirements and align with Responsible AI standards and practices. Partner with product and customers to understand scenarios, translate requirements into well-designed APIs and developer experiences, and drive adoption through documentation and samples. Bachelor's Degree in Computer Science or related technical field and 2+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, Python, or equivalent experience. These requirements include but are not limited to the following specialized security screenings: Master'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 OR 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. Advanced degree in Computer Science, Machine Learning, or related field. Experience with prompt engineering, retrieval-augmented generation (RAG), and memory/agent frameworks. Experience building and shipping generative AI systems (including multimodal scenarios). Familiarity with compliance and security standards in enterprise AI solutions. Track record of delivering enterprise-facing AI products at scale. Experience building and operating ML/AI systems in cloud environments; familiarity with MLOps practices (Azure a plus).