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About This Role
Accelerate the possible by joining a winning Amcor team that’s transforming the packaging industry and improving lives around the world.
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At Amcor, we unpack possibility through our innovative and responsible packaging to provide solutions that benefit our customers, our people and our planet. More than 10,000 consumers worldwide encounter our products every second and rely on us for safe access to food, medicine and other goods. We value their trust by making safety our guiding principle. It’s our core value and integral to how we do business.
Beyond this core principle, our shared values and behaviors unite us as we work together to elevate customers, shape lives and protect the future. We champion our customers and help them succeed. We play to win – adapting quickly in an everchanging world – and make smart choices to safeguard our business, our communities and the people we serve for generations to come. And we invest in our world\-class team, empowering our colleagues to unpack their potential, because we believe when our people grow, so does our business.
To learn more about playing for Team Amcor, visit www.amcor.com I LinkedIn I Glassdoor I Facebook I YouTube
Job Description
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About the role
The AI Innovation Engineer is responsible for defining, designing, and prototyping AI\-enabled products, applications, agents, and intelligent workflows that deliver measurable business value. Working independently or within a small team, the role develops innovative AI solutions while ensuring alignment with enterprise strategy, governance frameworks, scalability requirements, security standards, and responsible AI principles.
Key Job Accountabilities
- Lead the design, development, and prototype build of AI\-enabled products, applications, APIs, and intelligent workflows aligned with the project’s objectives.
- Integrate development of scalable AI solutions using Azure, Microsoft Fabric, LLMs, AI agents, APIs, semantic models, data pipelines, and orchestration frameworks.
- Define product requirements, user experiences, experimentation methods, and evaluation frameworks to deliver measurable business value.
- Implement necessary security, governance, CI/CD, testing, monitoring, observability, documentation, and operational support practices for enterprise AI solutions.
- Collaborate with business, engineering, and data stakeholders to drive innovation, user engagement, and secure, responsible AI practices.
Qualifications/Requirements
- Bachelor’s degree in a related technical or business field required; advanced degrees, Microsoft certifications, and AI\-assisted development tool experience preferred
- 2\+ years of experience in software engineering, data engineering, or related technical roles.
- Proficiency in C\#, Python, TypeScript/JavaScript, React, APIs, cloud architectures, ETL/ELT pipelines, data modeling, CI/CD, and modern software engineering practices.
- Proficiency using Claude Code or Codex for application development and data engineering
- Strong understanding of AI/ML concepts including LLMs, RAG, embeddings, copilots, prompt/context engineering, orchestration, experimentation, evaluation methods, and enterprise AI integration patterns.
- Experience with Microsoft Azure, Microsoft Fabric, Azure AI Foundry, Power Platform, and Git; manufacturing or supply chain experience in a multinational company is preferred.
Our Expectations
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We expect our people to be guided by The Amcor Way and demonstrate our Values every day to enable the business to win. We are winning when:
- Our people are engaged and developing as part of a high\-performing Amcor team
- Our customers grow and prosper from Amcor’s quality, service, and innovation
- Our investors benefit from Amcor’s consistent growth and superior returns
- The environment is better off because of Amcor’s leadership and products
Equal Opportunity Employer/Minorities/Females/Disabled/Veterans/Sexual Orientation/Gender Identity
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Amcor is an Equal Opportunity Employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability status, protected veteran status, or any other characteristic protected by law.
If you would like more information about your EEO rights as an applicant under the law, please click on the *"Know Your Rights: Workplace Discrimination is Illegal" Poster*. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the employment process, please call 224\-313\-7000 and let us know the nature of your request and your contact information.
E\-Verify
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We verify the identity and employment authorization of individuals hired for employment in the United States.
Benefits
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When you join Amcor, you will have access to a comprehensive benefits and compensation package that includes:
- Medical, dental and vision plans
- Paid vacation for full\-time employees
- Company\-paid holidays starting at 11 days per year
- Employee Assistance Program
- Health Savings Account/Flexible Spending Account
- Life insurance, AD\&D, short\-term \& long\-term disability, and voluntary accident and critical illness benefits are available
- Retirement Savings Plan with company match
- Discretionary annual bonus program (initial eligibility dependent upon hire date)
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amcor, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Amcor AI Hiring
Amcor has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Peachtree City, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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