Senior / Principal AI Engineer for Business Intelligence

About the position As a Sr./Principal AI Engineer within TSMC's Artificial Intelligence for Business Intelligence Innovation (AI4BII) Center, you will join an exciting global team dedicated to generating crucial business intelligence insights that shape TSMC's strategic decisions and global operations. This role uniquely blends applied research with end-to-end product development, placing you at the forefront of our mission to leverage advanced AI for a competitive edge. Operating with the agility of an internal startup, you will have the autonomy to build foundational systems from the ground up. You will tackle complex challenges spanning from advanced analytics and multimodal AI agents to time-series forecasting and reinforcement learning for manufacturing optimization. This is a role for a builder and a researcher who has been through the 'zero-to-one' product journey and thrives on rapid iteration, technical leadership, and seeing their work create tangible business impact. Our hybrid work schedule currently requires 4 days in the office, ensuring a dynamic and collaborative work environment.

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.

Requirements

• At least 7+ years of professional experience in software engineering, machine learning engineering, or related fields in high-performance environments.
• 5+ years of hands-on experience in professional software and/or machine learning engineering.
• 2+ 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).
• Deep, hands-on expertise in the modern AI stack, including RAG, fine-tuning, agentic frameworks, prompt engineering, vector databases, and model evaluation techniques.
• Strong fundamentals in backend engineering and distributed systems. Mastery of Python is required.
• Hands-on experience with at least one major cloud AI platform (GCP Vertex AI, AWS SageMaker, Azure ML) and containerized workflows (Docker, Kubernetes).
• 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.
• B.S. or higher in Computer Science, Engineering, Mathematics, or a related technical field. An M.S. or Ph.D. is a plus.

Nice-to-haves

• 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.

Benefits

• Market competitive pay
• Allowances
• Bonuses
• Comprehensive benefits
• Extensive development opportunities and programs

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