AI Full Stack Engineer

Role Overview

• 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);
• 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.
• Stay 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.

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, Kerasand 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)
• 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.

Tech Stack

• AWS
• Azure
• Cloud
• Django
• Docker
• Flask
• Google Cloud Platform
• GraphQL
• Java
• JavaScript
• Kubernetes
• Microservices
• Node.js
• NoSQL
• Postgres
• PySpark
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tableau
• Tensorflow

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)