Senior Machine Learning Ops Engineer

ML Ops Engineers are involved in setting the overall ML Ops strategy for the organisation as well as the management of complex projects. They will work closely with cross-functional teams including data scientists, engineers and business stakeholders to ensure that ML Ops initiatives are aligned with industry standards and business goals. The role of a Machine Learning OPs Engineer includes: Plans and drives the development of data engineering solutions ensuring that solutions balance functional and non-functional requirements Monitors application of data standards and architectures including security and compliance. Contributes to organisational policies, standards, and guidelines for data engineering. Given a well-described problem, assesses whether machine learning is likely to provide an effective solution. Implements algorithms and AI developed by Data Scientists. Advises on the effectiveness of specific techniques, based on project findings and wider research. Contributes to the development, evaluation, monitoring and deployment of machine learning systems. Understands and applies rules and guidelines specific to the industry and anticipates risks and other implications of modelling. Leads the assessment, analysis, planning and design of release packages, including assessment of risk. Liaises with business and technology teams on release scheduling and communication of progress. Conducts post-release reviews. Ensures that release processes and procedures are applied and that releases can be rolled back as needed. Identifies, evaluates and manages the adoption of appropriate release and deployment techniques, processes and automation tools. Key duties and responsibilities Duties may include some or all of the following: Build automated CI/CD pipelines to test, deploy, monitor and retrain models Implement ethical AI practices and assess privacy risks Work with data scientists and engineers on system integration, and with product teams on requirements. Communicating solution designs with product managers and business stakeholders. Staying current with new model architectures, and evolving MLOps best practices. Knowledge / Qualifications Advanced degree in computer science, statistics, mathematics, or a related field, with a focus on data science, machine learning, or artificial intelligence. Experience (3+ years) in AI/ML engineering with a proven track record Criteria The buyer has specified that each candidate must provide a one page pitch to address all criteria specified. This is equal to 5000 characters. Essential criteria 1. Data engineering: Level 5 (SFIA) Plans and drives the development of data engineering solutions ensuring that solutions balance functional and non-functional requirements. Monitors application of data standards and architectures including security and compliance. Contributes to organisational policies, standards, and guidelines for data engineering. 2. Machine learning: Level 4 (SFIA) Given a well-described problem and dataset, assesses whether machine learning is likely to provide an effective solution. Implements algorithms developed by others. Advises on the effectiveness of specific techniques, based on project findings and wider research. Contributes to the development, evaluation, monitoring and deployment of machine learning systems. Understands and applies rules and guidelines specific to the industry, and anticipates risks and other implications of modelling. 3. Release and deployment: Level 5 (SFIA) Leads the assessment, analysis, planning and design of release packages, including assessment of risk. Liaises with business and technology teams on release scheduling and communication of progress. Conducts post-release reviews. Ensures that release processes and procedures are applied and that releases can be rolled back as needed. Identifies, evaluates and manages the adoption of appropriate release and deployment techniques, processes and automation tools. 4. Testing: Level 5 (SFIA) Plans and drives testing activities across all stages and iterations of product, systems and service development. Provides authoritative advice and guidance on any aspect of test planning and execution. Adopts and adapts appropriate testing methods, automated tools and techniques to solve problems in tools and testing approaches. Measures and monitors applications of standards for testing. Assesses risks and takes preventative action. Identifies improvements and contributes to the development of organisational policies, standards, and guidelines for testing. Desirable criteria 1. Programming/software development: Level 3 (SFIA) Designs, codes, verifies, tests, documents, amends and refactors moderately complex programs/scripts. Applies agreed standards and tools to achieve a well-engineered result. Monitors and reports on progress. Identifies issues related to software development activities. Proposes practical solutions to resolve issues. Collaborates in reviews of work with others as appropriate. Opportunity summary Sellers can submit Up to 3 candidates Number of sellers invited More than 10 Number of candidates submitted Fewer than 10 Send us feedback About Accessibility Privacy Terms of use Disclaimer and copyright An initiative of the Digital Transformation Agency The Australian Government acknowledges the Traditional Owners of Country throughout Australia and acknowledges their continuing connection to land, waters and community. We pay our respects to the people, the cultures and the Elders past and present. © Commonwealth of Australia