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You’ll help build core generative AI and multimodal capabilities that power customer-facing AI services at global scale. In this role, you’ll combine strong software engineering with applied ML expertise to design, ship, and operate production systems—using techniques like context engineering, synthetic data generation, and rigorous evaluation/metrics to continuously improve quality. You’ll partner closely across product, research, and service engineering to deliver secure, performant improvements for enterprise customers.
Job Responsibility:
Design, build, and operate production-grade generative AI and multimodal systems, with end-to-end ownership from concept through deployment and service operations
Lead 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
Provide technical leadership across teams by setting direction, reviewing designs, unblocking execution, and mentoring engineers on architecture, coding standards, and ML engineering best 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
Requirements:
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, Python or equivalent experience
Advanced degree in Computer Science, Machine Learning, or related field
Demonstrated technical leadership through influence (e.g., leading designs, setting architecture direction, mentoring engineers)
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)
Experience partnering with cross-functional stakeholders to define requirements and drive technical decisions