AI Engineering and Architecture Lead

PharmaEssentia U.S.A.•Burlington, MA

3d•$190,000 - $245,000•Hybrid

About The Position

The AI Engineering & Architecture Lead owns PharmaEssentia’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.

Requirements

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 vendor to technical account on scope and quality
Still technical: able to review code, debug a production system, and set standards by example

Nice To Haves

Experience in pharmaceutical, biotech, or healthcare commercial environments, or another regulated industry with review and audit requirements
Experience standing up an AI engineering function from zero rather than inheriting one
Integration experience with CRM and field-facing systems (Veeva, Salesforce) and marketing platforms
Familiarity with evaluation, guardrail, and observability tooling for LLM applications
Experience in an organization where adoption, not delivery volume, was the measure of success

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