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Forward Deployed Engineer, AI

Arrowhead Pharmaceuticals
Madison, USA
Mid · 5+ years experience
Onsite
Discovered Today
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Requirements

  • Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
  • 5+ years of professional software engineering experience, including building and deploying production-grade applications.
  • Hands-on experience with AWS cloud services and Azure/Microsoft cloud services.
  • Experience working with Databricks, including data engineering, notebooks, and/or ML workflows.
  • Strong proficiency in at least one modern programming language (e.g., Python, SQL, or similar) used for building data and AI applications.
  • Demonstrated experience building agentic workflows and applications with generative AI and LLM-based approaches (e.g., orchestration frameworks, tool use, retrieval-augmented generation) to solve practical business problems.
  • Proven ability to work independently in ambiguous, undefined environments and to build solutions from the ground up without an established playbook.
  • Strong cross-functional communication skills, with the ability to translate technical concepts for non-technical business stakeholders and vice versa.
  • Preferred
  • Knowledge of the pharmaceutical, biotech, or life sciences industry is desirable but not required.
  • Experience with Databricks certifications, AWS certifications, or Microsoft Azure certifications.
  • Prior experience in a "forward deployed engineer," solutions engineering, or embedded engineering role, working directly with business teams rather than in a purely centralized product function.
  • Experience with LLMOps/agent-ops tooling and practices for deploying, monitoring, and evaluating agentic workflows in production.
  • Familiarity with regulated-industry considerations (e.g., data privacy, validation, GxP) relevant to deploying AI solutions in a pharma environment.
  • Experience building internal tools, chatbots, or agentic AI applications using modern LLM frameworks.
  • A builder mindset with the initiative to define scope and process in a green-field function.
  • Wisconsin pay range $155,000—$175,000 USDCalifornia pay range $175,000—$190,000 USD
  • Arrowhead provides competitive salaries and an excellent benefit package.
  • Candidates must have current, valid authorization to work in the country where this role is located.
  • California Applicant Privacy Policy

Preferred Qualifications

  • Preferred

Responsibilities

  • Partner directly with stakeholders across R&D, clinical, manufacturing, commercial, and other business functions to identify, scope, and prioritize opportunities for AI-driven internal solutions.
  • Design, build, and deploy agentic AI workflows and applications that solve real operational and scientific problems, working end-to-end from prototype through production.
  • Establish foundational engineering practices, standards, and reusable patterns for AI solution delivery, since this is the first role of its kind in the organization.
  • Work closely with the Data Platform Engineer to access, model, and productionize data needed for AI solutions across Databricks and AWS.
  • Collaborate with the DevOps Engineer to build reliable CI/CD pipelines, deployment processes, and monitoring for AI applications across AWS/ Databricks inside a MS environments.
  • Leverage the Databricks platform and AWS services (e.g., Bedrock, Lambda, S3) to build and orchestrate agentic AI workflows and applications, primarily by composing and integrating existing foundation models rather than training new ones.
  • Integrate solutions with Microsoft/Azure tools (e.g., Azure AI services, M365) where appropriate to meet business needs.
  • Rapidly prototype proof-of-concepts with internal teams, gather feedback, and iterate quickly to move validated solutions into production.
  • Document architecture, decisions, and delivery patterns to build a repeatable internal playbook for future forward-deployed engineering work.
  • Act as a hands-on technical resource embedded with business teams, translating ambiguous business problems into concrete technical solutions.

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