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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 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 collaborate 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 ownership from implementation through deployment and live-site operations
Contribute to the design and implementation of core GenAI capabilities (e.g., retrieval-augmented generation, context and memory, orchestration) and make data-driven tradeoffs across quality, latency, cost, and safety
Improve model and system quality using evaluation frameworks, experiment design, and production telemetry
build robust testing, monitoring, and regression coverage
Work with security, privacy, and compliance partners to build solutions that meet enterprise requirements and align with Responsible AI standards and practices
Collaborate with teammates through design reviews, code reviews, and debugging to unblock delivery and improve architecture, code quality, and ML engineering practices
Partner with product and customers to understand scenarios, translate requirements into well-designed APIs and developer experiences, and contribute to adoption through documentation and samples
Requirements:
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, or Python OR equivalent experience
3+ years of experience in software engineering, machine learning engineering, or applied AI (or equivalent experience)
Experience contributing to technical designs and delivering features in complex codebases (e.g., writing design docs, reviewing changes, and improving reliability/performance)
Experience building and shipping generative AI systems (including multimodal scenarios)
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
Ability to meet Microsoft, customer and/or government security screening requirements are required for this role
This position will be required to pass the Microsoft Cloud background check upon hire/transfer and every two years thereafter
Nice to have:
Advanced degree in Computer Science, Machine Learning, or related field
Experience with prompt engineering, retrieval-augmented generation (RAG), and memory/agent frameworks
Familiarity with compliance and security standards in enterprise AI solutions
Track record of delivering enterprise-facing AI products at scale