Applied AI Engineer

Amcor•Neenah, WI

About The Position

The Applied AI Engineer accelerates R&D innovation by applying AI solutions to improve decision making and engineering productivity across Amcor’s global Flexibles R&D organization. The role identifies high-value AI opportunities and develops scalable solutions, including knowledge discovery, intelligent automation, predictive modeling, and decision support. It works closely with R&D, IT, Information Security, Legal, and business stakeholders to promote responsible adoption, build resilient solutions, and deliver measurable business impact. The engineer also helps shape Amcor’s R&D AI strategy by evaluating emerging technologies and identifying ways to leverage technical knowledge, data, and insights across the organization. The position builds AI-enabled knowledge systems and decision-support capabilities; develops the data structures, semantic layers, metadata, and retrieval mechanisms needed to connect and interpret R&D information; and establishes testing, monitoring, and evaluation practices. It encodes R&D domain knowledge into AI infrastructure, addresses data gaps, and defines information architecture, knowledge-management standards, and governance approaches. The role also supports security and cost assessment, adoption, change management, and integration of AI capabilities into global R&D workflows.

Requirements

Degree in Computer Science, Data Science, Software Engineering, Engineering, Applied Mathematics, or a related technical discipline.
At least 3 years of industry experience in software engineering, data science, machine-learning engineering, or applied AI, including experience with systems running in production.
Demonstrated experience building applied AI solutions—such as large language model applications, machine-learning systems, intelligent automation, or knowledge-discovery tools—that achieved measurable business value and adoption.
Working familiarity with modern AI application building blocks, including retrieval and embeddings, prompt and context design, structured output, tool use, and evaluation.
Practical judgment regarding model limitations such as hallucination, retrieval failure, and evaluation, and experience designing systems that fail safely and visibly.
High proficiency in Python and SQL, including data pipelines and converting unstructured technical content into machine-usable form.
Proven delivery of cross-functional projects with measurable business impact.
Ability to travel occasionally, less than 10%.
Genuine interest in learning the relevant domain.

Nice To Haves

An advanced degree.
Understanding of polymer science, film extrusion, or food-packaging interactions.
Azure experience.

Responsibilities

Develop and deploy AI-enabled knowledge systems that connect structured and unstructured enterprise data and R&D content.
Deliver AI-assisted decision-support capabilities, including material and construction selection and product design validation.
Apply predictive modeling, intelligent automation, knowledge systems, and generative AI to business problems, selecting approaches based on value, practicality, and scalability.
Establish testing, monitoring, and evaluation practices to keep AI systems reliable, transparent, and aligned with responsible AI principles.
Evaluate emerging retrieval, generative AI, and applied machine-learning technologies and translate them into practical R&D applications.
Build and maintain data structures, semantic layers, metadata, and retrieval mechanisms for R&D information.
Encode R&D domain knowledge, including schema semantics, data relationships, and units, into AI infrastructure.
Identify and close data gaps that block AI capabilities and partner with data engineering and IT on general ingestion and pipeline work.
Define information architecture, knowledge-management standards, and governance approaches for reusable, machine-interpretable R&D knowledge.
Assess security exposure and cost before extending model access to new R&D systems and data, and determine how capabilities fit global R&D processes before broader rollout.
Partner with business and R&D functions to make models, recommendations, and insights accessible, explainable, and actionable for end users.
Work with R&D engineers to understand real design workflows and build solutions that support those workflows.
Drive adoption through stakeholder engagement, change management, and measurement of business impact and sustained usage.
Partner with IT, Information Security, and Legal on responsible AI deployment, confidential data protection, data residency, and acceptable use.
Translate technical concepts into actionable recommendations for multiple audiences.

Benefits

Medical, dental, and vision plans.
Flexible time off, starting at 80 hours paid time per year for full-time salaried employees.
Company-paid holidays, starting at 8 days per year and varying by location.
Wellbeing program and Employee Assistance Program.
Health Savings Account and Flexible Spending Account.
Life insurance, AD&D, short-term and long-term disability, and voluntary benefits.
Paid Parental Leave.
Retirement Savings Plan with company match.
Tuition Reimbursement, dependent upon approval.
Discretionary annual bonus program; initial eligibility depends on hire date.