AI ENGINEER

TaltentTrek; City of Melbourne; On-site; AUD 120,000 - 170,000; Full time

Job summary

TaltentTrek in Australia is seeking an AI Engineer to design, develop and deploy AI and machine learning solutions—from data preparation and model development to production integration and monitoring.

You will collaborate with software engineers, data scientists and business stakeholders to deliver practical, scalable AI, including NLP, prediction and image analysis, with emphasis on responsible AI and data privacy.

Qualifications

• Bachelor's degree or equivalent practical experience in computer science, AI, data science or related field.
• Strong Python programming skills and SQL experience.
• Hands-on knowledge of ML/deep learning concepts and model evaluation.

Responsibilities

• Design, develop and deploy AI/ML solutions to meet business requirements.
• Collect, clean and prepare data for model training, evaluation and testing.
• Build and optimise models for NLP, prediction and image analysis.
• Develop generative AI solutions, including AI assistants and document processing.
• Integrate AI models into applications via APIs.
• Evaluate model accuracy, reliability, fairness, response time and operating costs.
• Implement automated data pipelines for training, deployment, monitoring and maintenance.
• Monitor production models for performance issues and data drift; implement improvements.
• Apply data privacy, security and responsible AI practices throughout development.
• Collaborate with software engineers, data scientists and business stakeholders to deliver practical solutions.
• Maintain technical documentation, evaluation results and deployment procedures.

Skills

Python

SQL

ML concepts

Model evaluation

PyTorch

TensorFlow

scikit-learn

APIs

Git

Cloud platforms

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

Git

APIs

Cloud platforms

Job description

The AI Engineer designs, develops and deploys AI and machine learning solutions, from data preparation and model development through to production integration, monitoring and responsible AI practices.

What you'll do

• Design, develop and deploy artificial intelligence and machine learning solutions to meet business requirements.
• Collect, clean and prepare data for model training, evaluation and testing.
• Build and optimise models for applications such as natural language processing, prediction and image analysis.
• Develop generative AI solutions, including AI assistants, document processing and intelligent search.
• Integrate AI models and services into applications through APIs.
• Evaluate model accuracy, reliability, fairness, response time and operating costs.
• Implement automated pipelines for model training, deployment, monitoring and maintenance.
• Monitor production models for performance issues and data drift, and implement improvements.
• Apply data privacy, security and responsible AI practices throughout development.
• Collaborate with software engineers, data scientists and business stakeholders to deliver practical solutions.
• Maintain technical documentation, evaluation results and deployment procedures.

What we’re looking for

• Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Engineering or a related discipline, or equivalent practical experience.
• Strong programming skills in Python and experience with SQL.
• Practical knowledge of machine learning, deep learning, statistics and model evaluation.
• Experience with frameworks such as PyTorch, TensorFlow or scikit-learn.
• Ability to prepare datasets, engineer features and select appropriate modelling approaches.
• Experience deploying models and integrating them into software applications.
• Understanding of APIs, Git, software testing and development practices.
• Familiarity with cloud platforms such as AWS, Microsoft Azure or Google Cloud.
• Strong analytical, problem-solving, communication and documentation skills.
• Desirable: experience with large language models, prompt design, embeddings and retrieval-augmented generation (RAG).
• Desirable: familiarity with vector databases and AI application frameworks.
• Desirable: experience with model fine-tuning and systematic evaluation of generative AI outputs.
• Desirable: knowledge of MLOps, experiment tracking, model versioning and CI/CD pipelines.
• Desirable: experience with Docker and Kubernetes.
• Desirable: understanding of AI risks, including bias, hallucinations, prompt injection and data leakage.
• Desirable: relevant postgraduate qualifications, certifications or a portfolio of deployed AI projects.