LH-05000 - 2 Software Engineer (Developer) - AFP RFQ type DMP2 - ICT Labour Hire RFQ ID LH-05000 RFQ published date Friday, 07 November 2025 RFQ closing date Friday, 14 th November Estimated start date Monday, 05 January 2026 Initial contract duration 12 months Extension term Other Extension term details any number of periods not exceeding 12 months Number of extensions Buyer has not provided these details Maximum number of candidates per seller 2 Experience level APS6 equivalent Location of work VIC Working arrangements Onsite Maximum hours 40 hours per week Security clearance Negative Vetting Level 1 Job details Software Engineers write and test code, optimise software for speed and capability, evaluate and test new software, design and maintain software systems under limited supervision. Key duties and responsibilities The AiLECS Lab has a requirement for an experienced Senior Python Developer and one Junior Python Developer, developing and optimizing machine learning models and applications. The specified personnels will work with large datasets and collaborate with data scientists, engineers, and other stakeholders to drive machine learning projects from ideation to production and/or Data Engineers and research fellows. Senior Python Developer Minimum 5 years' experience in software development with Python Proven software development project management skills including ability to break down complex software development goals into Epics, User Stories, and Issues using agile project management tools (e.g. Jira, GitLab, GitHub Projects) Strong familiarity with software development principles and practices including developing CI/CD pipelines, version control, code repositories and testing frameworks Demonstrated experience building and/or fine-tuning Machine Learning models using contemporary AI/ML frameworks such as Pytorch, Tensorflow, Ultralytics, or HuggingFace. Sound understanding of different AI model architectures such as Transformers, CNNs and LLMs. Willingness to source, annotate and augment/transform data for model development purposes. Junior Python Developer Minimum 2 years' experience in software development with Python Proven software development project management skills including ability to break down complex software development goals into Epics, User Stories, and Issues using agile project management tools (e.g. Jira, GitLab, GitHub Projects) Strong familiarity with software development principles and practices including developing CI/CD pipelines, version control, code repositories and testing frameworks Demonstrated experience building and/or fine-tuning Machine Learning models using contemporary AI/ML frameworks such as Pytorch, Tensorflow, Ultralytics, or HuggingFace. Sound understanding of different AI model architectures such as Transformers, CNNs and LLMs. Willingness to source, annotate and augment/transform data for model development purposes. Essential criteria: Understanding of machine learning algorithms, data science frameworks, and programming expertise to create innovative AI solutions Implement machine learning algorithms from research papers and transform them into robust, scalable and secure software systems. Experience in evaluating and benchmarking machine learning model performance using appropriate metrics, tools, and comparative analysis. Integrating AI systems. Deploy AI-powered applications into existing workflows and ensure seamless integration with APIs and platforms. Conducting data preprocessing. Clean, prepare and augment large datasets to train and fine-tune AI models. Demonstrated experience developing APIs to integrate disparate applications in an enterprise environment to provide end-to-end business solutions Experience using collaboration tools that include git, issue tracking and wikis (e.g. GitLab, GitHub, Azure DevOps) Desirable criteria: Deep Learning Frameworks for building, training, and deploying neural network models (PyTorch and/or TensorFlow) Machine Learning Libraries for model development and fine-tuning (e.g. NumPy, Hugging Face, Ultralytics) ML Tooling & MLOps Platforms for experiment tracking, model management, and reproducibility (e.g. Weights & Biases (W&B) and/or MLflow) Python web application framework (e.g. Django, FastAPI, Flask) Workflow orchestration (Nifi, Prefect, Airflow) Development and consumption of REST APIs / Micro-service architectures Front-end development (e.g. JavaScript, HTML and CSS) Containerisation technologies (e.g. Docker, Kubernetes) Knowledge and use of CI/CD pipelines (e.g. Git, Ansible) Working with Agile methodologies (e.g. Kanban, Scrum) To summarise the role: Being able to code up a model from a research paper Being able to evaluate model performance using appropriate metrics (this is important) Knowledge of MLOps to track/manage model development, evaluation and deployment Familiarity with how to use the main ML Python libraries: In particular, Pytorch, Numpy, Hugging Face, Ultralytics (YOLO and DETR). Criteria The buyer has specified that each candidate must provide a response to each criterion. Each response is limited to 3000 characters / 500 words. Highlighted in yellow. Respond in third person. Recommended 2-4 paragraphs Example - Understanding of machine learning algorithms, data science frameworks, and programming expertise to create innovative AI solutions Tim has a strong understanding of machine learning algorithms and data science frameworks, applying these skills to deliver practical AI-driven solutions in real-world environments. For example, he developed a predictive maintenance model using Python, scikit-learn, and TensorFlow to forecast equipment failures within Defence systems, reducing downtime by over 25%. He also implemented a computer vision pipeline leveraging OpenCV and PyTorch to automate the classification of high-resolution sensor imagery, significantly improving data analysis efficiency. In addition, Tim has applied his expertise in data preprocessing, feature engineering, and model optimisation to improve model accuracy and reliability. His programming proficiency extends to Python, C++, and SQL, enabling seamless integration of AI models into existing enterprise systems. Through these projects, Tim has demonstrated a strong capability to translate theoretical machine learning concepts into innovative and operationally effective AI solutions.
