Company Description

Headquartered in Australia, Viable Solutions is a technology services organisation focused on providing purpose-built intelligent automation solutions. We specialise in innovative technologies to optimise and automate processes, enhancing efficiency and performance for businesses. Our mission is to deliver cutting-edge tech advancements that drive success and transformation. Join us in shaping the future of intelligent automation.

Role Description

This is a full-time hybrid role for an AI and ML Engineer based in Sydney, NSW or Melbourne, VIC, with some work-from-home flexibility. The AI and ML Engineer will be responsible for designing, developing, and deploying machine learning models and algorithms. Day-to-day tasks include researching and implementing new machine learning techniques, collaborating with cross-functional teams to integrate AI solutions, ensuring the scalability and efficiency of models, and analysing large data sets for pattern recognition and insights. The role involves continuous learning and adapting to new AI trends and technologies.

Responsibilities

• Apply expertise across the Generative AI, NLP and machine learning stack, in areas such as statistics and data analysis, data engineering and pre-processing, model selection, training, tuning and deployment, and solution architecture and engineering
• Scope and plan data science and machine learning projects to achieve the most optimal and practical outcomes
• Liaise with and manage project stakeholders to deliver a valuable solution
• Design and implement data analytics solutions
• Undertake modelling, movement, wrangling and processing of data to deliver customer solutions
• Coordinate with product teams to understand business needs, and understand how they translate to functional requirements and foundational LLM prompts
• Help deploy new builds and features into pilot and production environments and work closely to capture feedback and CI opportunities from end users
• Help testing of application code, including writing unit test plans
• Support efforts to drive innovation through out-of-the-box thinking to solve critical business challenges and demands
• Participate on technical discovery, POCs, and innovation work streams to validate new tools, technologies, and designs
• Investigate and keep up-to-date on emerging and cutting-edge data technologies and trends, lead knowledge sharing 

Qualifications

• Bachelor or Masters degree in Computer Science/ Software Engineering or a related field, or related experience
• 3-5 years of engineering experience, with experience leading AI/GenAI projects and evidence of multiple successful projects or research/open-source contributions.
• Advanced proficiency in Python and experience writing quality production code (familiarity with other relevant languages is a bonus).
• Experience with cloud platforms (e.g. Azure, Databricks, AWS etc) and associated machine learning products, e.g. Amazon SageMaker, Azure ML, Azure AI Foundry
• Experience in developing data products and solutions (leveraging GenAI/ ML techniques preferred)
• Experience with GenAI frameworks and tools (e.g. Retrieval Augment Generation, AgenticAI, NLP) 
• Experience in constructing or contributing to a Responsible AI framework.
• Understanding of probabilistic programming techniques and associated tools (e.g. Pyro, Stan, Tensorflow Probability, PyMC3), Bayesian inference and MCMC methods 
• Strong understanding of fundamental computer science concepts, particularly data structures, algorithms, automated testing, object-oriented programming, performance complexity, and implications of computer architecture on software performance 

Mandatory:

• Experience with containers, e.g. Docker and Git 
• Proficiency in one or more of Python, Java, C++, Scala, Go, Julia. Familiarity with JavaScript Frameworks e.g. React, Vue
• Track record of implementing statistical, machine learning and NLP models, deploying these and maintaining them in production environments 
• Ability to develop data products and solutions
• Solid understanding of foundational concepts and algorithms in statistics and machine learning, including linear/logistic regression, SVM, random forest, boosting, neural networks, dimensionality reduction, reinforcement learning, etc. 
• Strong interpersonal and communication skills, including the ability to explain and discuss the technicalities of ML algorithms and techniques with colleagues and clients from other disciplines 
• Ability to create and deliver project presentations and results to stakeholders in a clear and professional manner
• Ability to work closely with internal and customer teams
• Ability to manage own time and prioritise tasks
• Ability to provide meaningful effort estimations and appropriately escalate risks or dependencies
• Strong sense of ownership over tasks and investment in quality
• Ability to work in multi-disciplinary teams to deliver high-impact projects
• Ability to work in an Agile environment with Project Managers & Product Owners, and familiarity with Agile software development practices
• Strong interpersonal and communication skills, including proven ability to communicate problems and solutions to technical and non-technical stakeholders
• Strong collaborator with ‘can-do’ attitude