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# Engineer, AI Applications

- **Company:** Pharmaessentia Corporation
- **Location:** Burlington, MA, United States
- **Experience:** Expert
- **Salary:** $140,000.0 - $182,000.0
- **Contract:** Permanent contract
- **Skills:** Sql Data Warehouse, Artificial Intelligence, Python (Programming Language), Software Product Management, Salesforce.Com, SQL Databases, Systems Integration, Large Language Models, Snowflake, Multi-Agent Systems, Generative AI, AI Platforms, Information Technology, Veeva
- **Published:** October 12, 2026
- **Apply:** https://www.juju.com/job/15_d237b0a8

## About the Role

* 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 experience with retrieval, context management, tool and API integration, and agent orchestration frameworks
 * Has built evaluation and monitoring for an AI system, and can explain how quality was measured rather than asserted
 * Experience deploying and operating services in a cloud environment, including observability and incident response, * Experience in pharmaceutical, biotech, or healthcare commercial data, or another regulated industry
 * Integration experience with CRM and field-facing systems (Veeva, Salesforce)
 * Enough front-end capability to ship a usable interface without waiting for a dedicated front-end hire
 * Experience as an early engineer on a small team, where scope is set by what the business needs rather than by a backlog handed down

Location and Work Arrangement
This position is based in Burlington, Massachusetts, and follows a hybrid work model requiring on-site presence three days per week.

## Description

Build

 * 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 Patterns

 * 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

Partner

 * 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