AI Engineering and Architecture Lead

About the role

PharmaEssentia Corporation is a rapidly growing biopharmaceutical innovator delivering effective new biologics for hematology and oncology, with one approved product and a diversifying pipeline. Founded in 2003, the company is listed on the Taiwan Stock Exchange (TWSE: 6446) and operates globally with a biologics production facility in Taichung. The AI Engineering & Architecture Lead owns PharmaEssentia’s AI architecture and leads the engineering team that builds on it, reporting to the Head of Global Data & AI Enablement. This role creates the dedicated AI engineering capability called for in the Global Data & AI Enablement (GDAI) plan, turning governed data assets into production AI applications and agents that support commercial teams — opportunity identification, proactive field alerts, HCP intelligence, field copilots, next‑best‑action workflows, and omnichannel intelligence.

Responsibilities

• Define the AI and application architecture — retrieval, orchestration, agent frameworks, evaluation, observability, and integration patterns — across the GDAI portfolio
• Make and defend build‑versus‑buy decisions, retaining enough internal capability to own the architecture rather than depending on vendors for judgment or prioritization
• Design for the data environment as it is: vendor‑delivered raw data in Snowflake, a semantic layer built under internal oversight, and a roadmap to internalize both over time
• Lead delivery of production AI applications and agents against the Commercial roadmap, from first use case through sustained operation
• Prioritize engineering capacity with AI Product Strategy & Value so that every major initiative has an owner, a business case, and a measured outcome
• Replace dashboard search with proactive delivery: systems that detect meaningful signals and push the right insight to the right person at the right time
• Set standards for evaluation, testing, monitoring, cost control, security, and responsible deployment, and enforce them across internal and outsourced work
• Establish how AI output destined for field‑facing use is reviewed, logged, and kept within promotional, privacy, and medical–commercial separation requirements
• Evaluate and integrate vendor platforms, and hold vendors to technical account against architecture, security, and quality standards
• Build and mentor the AI engineering team — one senior engineer in the first wave, a second engineer subject to demonstrated adoption and value

Qualifications

Required:

• Bachelor’s degree required; technical degree (computer science, engineering, or related field) preferred
• 10+ years in software or ML engineering, including 3+ years leading engineers
• Has shipped and operated LLM‑based or agentic applications in production — not prototypes, proofs of concept, or pilots — and can describe what broke and what it cost
• Hands‑on ownership of retrieval, orchestration, evaluation harnesses, monitoring, and cost control for AI systems in production
• Experience architecting applications on a cloud data platform, ideally Snowflake
• Demonstrated build‑versus‑buy decisions, and experience holding a technology