Responsibilities
- Lead System Architecture: Own the end-to-end design and development of new AI-native products and platforms, from initial concept and data pipelines to scalable, production-grade services.
- Build with Frontier AI: Drive the hands-on implementation of advanced AI systems leveraging frontier LLM models, including the design of robust Retrieval-Augmented Generation (RAG) pipelines and multi-agent workflows.
- Prototype and Validate: Lead rapid validation sprints to build proof-of-concepts, create evaluation harnesses to measure accuracy, latency, and cost, and partner with product teams to harden prototypes for production release.
- Engineer for Scale: Architect and implement the underlying MLOps infrastructure, including model serving, automated testing, and observability, to ensure our AI services meet enterprise-grade SLAs.
- Drive Innovation: Research and validate novel AI use cases (e.g., threat-hunting copilots, developer productivity tools, automated workflow optimization) and build the foundational frameworks to accelerate their deployment.
- Collaborate and Mentor: Partner closely with Product, Design, and business stakeholders to ensure solutions are technically sound and commercially impactful. Mentor junior engineers and foster a culture of experimentation, responsible AI, and first principles thinking.
- Communicate Vision: Craft and deliver compelling executive-level narratives, demos, and visualizations that clearly communicate technical strategy, roadmaps, trade-offs, and business impact.
Basic qualifications
- Experience: At least 10+ years of professional experience in software engineering, machine learning engineering, or related fields in high-performance environments. This should include:
- 7+ years of hands-on experience in professional software and/or machine learning engineering.
- 3+ years of experience in a technical leadership role , specifically focusing on architecting and building scalable systems powered by Generative AI or Large Language Models (LLMs).
- Technical Expertise:
- Generative AI & LLMs: Deep, hands-on expertise in the modern AI stack, including RAG, fine-tuning, agentic frameworks, prompt engineering, vector databases, and model evaluation techniques.
- Backend & Systems Design: Strong fundamentals in backend engineering and distributed systems. Mastery of Python is required.
- MLOps & Cloud: Hands-on experience with at least one major cloud AI platform (GCP Vertex AI, AWS SageMaker, Azure ML) and containerized workflows (Docker, Kubernetes).
- Leadership & Communication:
- Demonstrated ability to translate ambiguous business problems into clear technical blueprints and phased execution plans.
- Exceptional communication and presentation skills, with experience conveying complex technical concepts to both engineering teams and senior management audiences.
- Education:
- B.S. or higher in Computer Science, Engineering, Mathematics, or a related technical field. An M.S. or Ph.D. is a plus.
Preferred qualifications
- Experience deploying AI into developer tooling (IDE plug-ins, CI/CD pipelines).
- Active contributions to open-source AI/ML projects or publications in top-tier academic conferences.
- A broad knowledge of mathematical modeling beyond ML, such as combinatorial optimization or operations research.
- The world’s leading dedicated semiconductor foundry
- The technology leader with a strong reputation for manufacturing excellence
- Advancing semiconductor manufacturing innovations to enable the future of technology
Tags & focus areas
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