Senior DevOps Engineer

Role: 1 x EL1 Data Science/DevOps Engineer The EL1 Data Science DevOps Engineer is accountable under broad direction for providing engineering leadership that enables DaSH to design, build, test and deploy AI and data science solutions safely and effectively. The role is crucial to moving DaSH capabilities from discovery and proof of concept through to pilot, production-readiness and reusable technical patterns across the portfolio. The role leads a small group of dedicated engineering specialists spanning Machine Learning, DevOps and Data Engineering. It is responsible for establishing practical engineering approaches, development practices, technical guardrails and reusable patterns that help DaSH teams deliver scalable, maintainable and responsible AI and data science solutions. The role works in close partnership with EL1 Lead Data Scientists, the Delivery Lead, Data Scientists, Business Analysts, technology teams and business stakeholders. Lead Data Scientists remain central to the analytical design, modelling approach and solution quality of individual data science initiatives. The Data Science & AI Engineering Lead complements this by providing the engineering leadership needed to productionise models, implement reliable data pipelines, support deployment pathways, and create common AI system patterns for broader reuse. The role contributes to the design and delivery of AI and data science system patterns in AWS and other approved Agency technology environments. This includes patterns for model deployment, retrieval-augmented generation, data preparation, feature engineering, evaluation workflows, monitoring, security, privacy, automation and integration with Agency platforms. The role is expected to balance hands-on technical leadership with team leadership, engineering and capability uplift. It will help DaSH create repeatable ways to deliver AI and data science solutions, reducing reliance on bespoke prototypes and supporting a clearer path from experimentation to operational value. Supplier Briefing: A supplier briefing will be held for this role on Monday, 28 September – meeting details will be posted in the “Q&A” section of the RFQ for invited Sellers only. Rate: The proposed rate should reflect candidate's skills and experience against the Evaluation Criteria. Candidates that do not demonstrate these criteria will not be assessed. Candidates should be aware that rates may be negotiated further as part of the selection process. Citizenship: As part of the eligibility and suitability requirement, NDIA seeks Labour Hire Workers who are Australian citizens only. Successful candidates will be required to furnish valid evidence of citizenship during the Pre-engagement Check. Labour Hire Licence: Applicable for ACT, VIC and QLD: Labour hire licences are required in the state that specified personnel are being contracted. Key duties and responsibilities Responsibilities of the role include but are not limited to: AI and data science engineering leadership Lead the design and delivery of engineering approaches that enable DaSH to build, test, deploy and maintain AI, machine learning and data science solutions. Provide technical leadership across AI engineering, machine learning engineering, DevOps and data engineering practices within DaSH. Lead several dedicated engineering roles spanning Machine Learning, DevOps and Data Engineering, supporting capability uplift, work planning and technical delivery. Work with EL1 Lead Data Scientists to translate analytical approaches and model designs into robust, secure and scalable implementation patterns. Ensure engineering decisions support delivery outcomes, maintainability, responsible AI obligations and operational readiness. Pathways to production and operational readiness Design and support clear pathways for DaSH prototypes and proofs of concept to progress toward pilot, production-readiness and operational implementation. Develop repeatable patterns for packaging, deploying, monitoring and maintaining data science and AI capabilities in approved Agency environments. Work with technology, platform, cyber security, privacy, data governance and operational teams to support readiness for production deployment. Advise on technical dependencies, non-functional requirements, release considerations, support models and handover requirements for AI and data science solutions. Help reduce the gap between prototype development and sustainable product delivery across the DaSH portfolio. AWS and reusable AI system patterns Contribute to the design of reusable technical patterns for deploying AI and data science solutions into AWS and other approved Agency technology environments. Design patterns for model serving, retrieval-augmented generation, document intelligence, data pipelines, evaluation workflows, monitoring, logging and secure integration. Establish reusable templates, reference architectures, technical guides and worked examples that can be shared across DaSH initiatives. Identify opportunities to standardise common technical components across multiple DaSH products and prototypes. Collaborate with Agency architecture, platform and engineering teams to ensure DaSH patterns align with broader technology standards and constraints. Data engineering, ML engineering and DevOps practices Lead the development of data preparation, feature engineering, data validation and pipeline approaches that support AI and data science delivery. Promote sound machine learning engineering practices, including version control, testing, experiment tracking, model evaluation, reproducibility and deployment automation. Support DevOps and MLOps practices that enable reliable development, release and monitoring of AI and data science capabilities. Advise on data quality, data access, labelling, synthetic data, test data and environment requirements for AI and data science initiatives. Contribute to technical controls that support privacy, security, explainability, human oversight and responsible AI requirements. Collaboration with Lead Data Scientists and delivery teams Work closely with EL1 Lead Data Scientists to ensure engineering approaches reflect the analytical intent, model design and evaluation requirements of each initiative. Collaborate with the Delivery Lead to ensure engineering work is visible, prioritised and integrated into delivery plans, sprint activities and portfolio reporting. Support multidisciplinary squads by translating technical complexity into practical delivery options, risks, assumptions and decision points. Provide engineering advice during discovery, scoping, proof of concept planning, technical assessment and senior decision-making processes. Contribute to a collaborative DaSH operating model where analytical leadership, engineering leadership and delivery facilitation work together to deliver business outcomes. Technical governance and capability uplift Develop technical guides, best practice documentation and reusable assets that uplift AI and data science engineering capability across DaSH. Provide specialist advice on model integrations, prompt orchestration, AI workflow automation, data pipelines, model evaluation infrastructure and AI-enabled system design. Contribute to technical assessments of emerging AI technologies, hosted models, open-weight models, tooling, deployment approaches and engineering practices. Participate in knowledge-sharing, innovation forums and AI communities of practice, representing DaSH engineering perspectives where required. Support compliance with Agency governance, privacy, cyber security, data classification, accessibility and responsible AI requirements. (NOTE: the key responsibilities of the role are based on current priorities and may change over time) 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. Demonstrated experience with Amazon Web Services (AWS) and the delivery of solutions into AWS that are Data Science, Machine Learning or AI in nature. 2. Demonstrated experience providing technical or engineering leadership for complex data science, machine learning or AI cloud-based solutions. 3. Demonstrated experience designing or implementing pathways from prototype or proof of concept through to production-readiness, operational deployment or sustainable product delivery. 4. Experience leading or coordinating technical specialists across one or more of machine learning engineering, DevOps, data engineering, AI engineering or cloud engineering. 5. Strong understanding of engineering practices relevant to AI and data science delivery, including data pipelines, automated testing, deployment automation, monitoring, version control and reproducibility. 6. Demonstrated ability to work collaboratively with Lead Data Scientists, delivery leads, business analysts, platform teams and business stakeholders to deliver technically sound and business-aligned outcomes. Desirable criteria 1. Familiarity with MLOps, LLMOps, DevOps, infrastructure as code, CI/CD, model evaluation, monitoring, observability or automated quality controls. 2. Experience designing or implementing retrieval-augmented generation, document intelligence, model serving, data pipeline or AI workflow automation patterns. 3. Familiarity with responsible AI, AI assurance, privacy, security, accessibility, human-in-the-loop design and data governance requirements. 4. Experience producing technical documentation, reference architectures, engineering standards, reusable implementation guidance, or communicating technical designs and trade-offs to technical and non-technical audiences. Opportunity summary Sellers can submit Up to 4 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. 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