AI Engineering and Architecture Lead

It’s not often you get the chance to make a real impact on the lives of others, while expanding your own possibilities. You’ll find that rare opportunity n us, and let’s transform lives, together. Pharma Essentia Corporation is a rapidly growing biopharmaceutical innovator. We are leveraging deep expertise and proven scientific principles to deliver effective new biologics for challenging diseases in the areas of hematology and oncology, with one product approved and a diversifying pipeline.

We believe in the potential to improve both health and quality of life for patients with limited options today through the combination of rigorous research and innovative thinking. Founded in 2003 by a team of Taiwanese-American executives and renowned scientists from U.S. biotechnology and pharmaceutical companies, today we are listed on the Taiwan Stock Exchange (TWSE: 6446) and are expanding our global presence with operations in the U.S., Japan, Singapore, South Korea, and China, along with a world-class biologics production facility in Taichung.

The AI Engineering & Architecture Lead owns Pharma Essentia’s AI architecture and leads the engineering team that builds on it. This is the most important single hire in the Global Data & AI Enablement (GDAI) plan: the team today has no dedicated AI engineering capability, and this role creates it.

Reporting to the Head of Global Data & AI Enablement, this leader is accountable for turning governed data assets into production AI applications and agents that change how commercial teams work — opportunity identification, proactive field alerts, HCP intelligence, field copilots, next-best-action workflows, and omnichannel intelligence. Backend ETL is delivered by an external vendor into Snowflake, so this role’s value sits above that layer: architecture, build-versus-buy judgment, engineering standards, and delivery.

Success is measured by sustained adoption and business outcomes, not by the number of tools launched.

• 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

• Bachelor’s degree…