Senior Engineer, AI

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 Senior Engineer, AI Applications is the hands‑on builder of Pharma Essentia’s first production AI applications and agents. Reporting to the AI Engineering & Architecture Lead within Global Data & AI Enablement (GDAI), this engineer writes the code that converts governed data assets into tools commercial teams use every day.

The first use cases are concrete: patient and HCP opportunity identification, and proactive field alerts that surface meaningful signals instead of asking the field to search dashboards. Backend ETL is delivered by an external vendor into Snowflake, so this role builds on vendor‑delivered and internally modeled data rather than owning ingestion. The patterns this engineer establishes — retrieval, orchestration, evaluation, monitoring — become the foundation the rest of the team builds on.

• Build agentic and LLM‑based applications against our Snowflake data assets, from first working version through production operation
• Implement retrieval, orchestration, evaluation, and monitoring so that application behaviour is measurable and regressions are caught before users see them
• Deliver early high‑value use cases, starting with opportunity identification and proactive field alerts, and iterate on them with real users
• Integrate AI services with the systems the field already uses, so output arrives in workflow rather than in a separate tool

• Establish reusable engineering patterns — prompt and agent structure, evaluation harnesses, deployment, observability — that the team can build on
• Write the documentation, tests, and deployment processes that let a second engineer work on the same codebase without archaeology
• Apply the standards set by the AI Engineering & Architecture Lead for security, cost, and responsible deployment, including how AI output reaching field‑facing use is reviewed and logged

• Work with Data Product Engineering to specify the data models and semantic assets applications need, and to flag where delivered data is not fit for purpose
• Work with AI Product Strategy & Value to turn prioritized business problems into shippable scope, and with AI Enablement & Adoption on rollout and user feedback
• Say plainly when a use case will not work as specified, and propose what would

• Bachelor’s degree required; technical degree (computer science, engineering, or related field) preferred
• 5+ years building production software, including recent hands‑on delivery of LLM‑based or agentic applications that real users depend on
• Strong Python and SQL; comfortable working directly against a cloud data warehouse, ideally Snowflake
• Practical…