Forward Deployed Engineer, AI

Arrowhead Pharmaceuticals•San Diego, WI

About The Position

Arrowhead Pharmaceuticals is hiring its first Forward Deployed Engineer, AI for a newly created, green-field role. The engineer will work directly with business and scientific stakeholders across multiple functions to identify high-value problems and build and deploy AI-driven solutions. With no existing playbook for the function, the role will help define how internal AI solutions are scoped, built, and delivered. The position is hands-on across AWS, Azure, Databricks, and Microsoft technologies, collaborating with the Data Platform Engineer, DevOps Engineer, and platform teams. The engineer will embed with internal teams to understand their workflows, move from problem discovery to working solutions, and build agentic AI applications primarily by composing and integrating existing foundation models rather than training new ones. The role also establishes reusable engineering practices and documents delivery patterns to support future work.

Requirements

Bachelor's degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
At least 5 years of professional software engineering experience, including building and deploying production-grade applications.
Hands-on experience with AWS cloud services and Azure or Microsoft cloud services.
Experience working with Databricks, including data engineering, notebooks, and/or ML workflows.
Strong proficiency in at least one modern programming language, such as Python or SQL, used to build data and AI applications.
Demonstrated experience building agentic workflows and applications using generative AI and LLM-based approaches, such as orchestration frameworks, tool use, or retrieval-augmented generation, to solve practical business problems.
Ability to work independently in ambiguous, undefined environments and build solutions from the ground up without an established playbook.
Strong cross-functional communication skills, including the ability to translate technical concepts for non-technical business stakeholders and vice versa.

Nice To Haves

Knowledge of the pharmaceutical, biotech, or life sciences industry.
Databricks, AWS, or Microsoft Azure certifications.
Prior experience in a forward deployed engineer, solutions engineering, or embedded engineering role working directly with business teams.
Experience with LLMOps or agent-ops tooling and practices for deploying, monitoring, and evaluating agentic workflows in production.
Familiarity with regulated-industry considerations such as data privacy, validation, and GxP in deploying AI solutions in a pharmaceutical environment.
Experience building internal tools, chatbots, or agentic AI applications using modern LLM frameworks.
A builder mindset and initiative to define scope and process in a green-field function.

Responsibilities

Partner 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, taking solutions from prototype through production.
Establish foundational engineering practices, standards, and reusable patterns for internal AI solution delivery.
Work with the Data Platform Engineer to access, model, and productionize data 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 and Databricks in Microsoft environments.
Use Databricks and AWS services such as Bedrock, Lambda, and S3 to build and orchestrate agentic AI workflows and applications, primarily by integrating existing foundation models.
Integrate solutions with Microsoft and Azure tools, such as Azure AI services and M365, where appropriate.
Rapidly prototype proof-of-concepts with internal teams, gather feedback, and iterate to move validated solutions into production.
Document architecture, decisions, and delivery patterns to create a repeatable internal playbook.
Embed with business teams as a hands-on technical resource and translate ambiguous business problems into concrete technical solutions.