AI Full Stack Engineer
About The Position
An AI Full Stack Engineer is responsible for designing, developing, and deploying machine learning, AI models and algorithms to enhance products and services. This role collaborates with key business data owners, data governance, data engineering, and IT application business analysts to align strategies, maintain execution transparency, and support effective delivery. This role leads the design and execution of regional or multi-site AI/ Gen AI/ML Models that drive operational efficiency, sustainability, and supply chain visibility. It defines and manages AI/ML roadmaps within a business domain, manages cross-functional teams, and translates business needs into scalable data solutions. The role operates with moderate autonomy, influences data architecture decisions, and ensures alignment with global standards. Skilled in cloud platforms, data modelling, and stakeholder engagement. In addition to AI/ML expertise, this role encompasses full stack development — building and maintaining frontend user interfaces, RESTful and GraphQL APIs, backend services, and database layers — to deliver end-to-end AI-powered applications from model to production UI.
Requirements
- Bachelor’s in computer science, Machine Learning, or a related field.
- 2 - 4 years’ experience as an AI/ML Engineer or in a similar role, with a strong understanding of machine learning algorithms and principles.
- Experienced in Large Language Models, Transformers, CNN, Scikit-learn, NLP libraries, Embedding Models, Vector Databases, AI Agents, and Agentic orchestrations.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch, Keras.
- Proficiency in programming languages like Python, PySpark, R, or Java.
- Experience with data visualization tools (Power BI and Tableau).
- Proficiency in modern frontend web technologies and frameworks (e.g. component-based UI libraries, HTML5, CSS3) to build responsive, user-friendly interfaces for AI-powered applications.
- Experience designing and building RESTful and/or GraphQL APIs using Python-based frameworks (e.g. FastAPI, Flask, Django) or Node.js; understanding of microservices architecture and API security best practices.
- Hands-on experience with relational databases (e.g. SQL Server, PostgreSQL) and NoSQL/vector databases; ability to design schemas, write optimized queries, and manage data pipelines that feed AI applications.
- Familiarity with containerization (Docker, Kubernetes), CI/CD tooling, and infrastructure-as-code on major cloud platforms (Azure, AWS, or GCP) to deploy and operate full stack AI solutions reliably at scale.
Responsibilities
- Design and develop machine learning models and algorithms tailored to specific business needs.
- Design and implement production‑ready RAG systems that connect LLMs to enterprise data sources (data lake, Microsoft Fabric, SAP, MES, document repositories); own prompt templates, retrieval strategies, and evaluation; optimize for accuracy, latency, and cost.
- Build AI agents that orchestrate multi‑step workflows.
- Work closely with data scientists, software engineers, and cross-functional teams to integrate models into production systems.
- Analyze large and complex datasets to extract actionable insights and improve model performance.
- Tune and enhance model performance and accuracy through iterative testing and validation.
- Maintain thorough documentation of AI/ML models, experiments, and processes to ensure reproducibility and knowledge sharing.
- Keep abreast of the latest advancements in AI and ML technologies to apply innovative solutions in projects.
- Establish and maintain working relationships with Amcor business stakeholders.
- Engage enterprise decision makers and stakeholders to facilitate group decisions and outcomes.
- Design and build end-to-end AI-powered applications encompassing frontend user interfaces, backend APIs, and integration layers that surface AI/ML model outputs to end users.
- Develop and maintain RESTful and/or GraphQL APIs and backend microservices that connect AI/ML models to enterprise data sources and front-end applications.
- Design, implement, and optimize relational and NoSQL database schemas to support AI application data needs, including vector databases for embedding storage and retrieval.
- Deploy and manage full stack applications on cloud platforms (Azure/AWS/GCP); implement CI/CD pipelines, containerization (Docker/Kubernetes), and infrastructure-as-code practices to ensure reliable, scalable delivery of AI solutions.
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 may vary by location
- Wellbeing program & Employee Assistance Program
- Health Savings Account/Flexible Spending Account
- Life insurance, AD&D, short-term & 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 dependent upon hire date)
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
