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Support

Engineer - AI Position Description: Data & Cloud Operations Support 1.

Primary

Function • Operate, support and maintain cloud-based AI solutions, leveraging automation and standardised operational processes.

- Manage cloud environments in accordance with company security guidelines, standards and platform requirements.
- Support, troubleshoot and resolve incidents affecting data applications, pipelines, cloud infrastructure and production services.
- Conduct post incident reviews and problem rectification, implementing corrective and preventative actions to reduce incident recurrence.
- Build, modify and support data pipelines to accommodate operational requirements and changes in data structures.
- Review AI pipelines, code and deployments from a support and operational perspective to ensure consistency, quality and production readiness.
- Work collaboratively with Engineering, Development and upstream/downstream system teams to resolve production issues and minimise impacts to applications and key SLAs.
- Liaise with business stakeholders, platform teams, cloud vendors and service providers to investigate and resolve application, infrastructure and production issues.
- Maintain and continuously improve support procedures, operational documentation, troubleshooting guides and production best practices.
- Identify gaps in existing support and operational processes and contribute to service, reliability and process improvements.
- Support cloud deployments and production changes, ensuring appropriate operational controls and best practices are followed.
- Provide technical support and guidance to stakeholders on data-platform applications, services and cloud technologies.
- Work Experience • Experience working in Cloud Operations, Production Support, AI Operations or a similar technical support environment.
- Demonstrated experience troubleshooting and analysing AI pipelines, cloud applications, DevOps pipelines and production workloads.
- Strong understanding of data warehousing concepts,



including Data Modelling, Agentic AI Solutions, Chatbots, data reconciliation, error handling and data lifecycle concepts.
- Working knowledge of cloud data platforms and technologies, preferably including Microsoft Azure, Snowflake and Databricks.
- Experience supporting Azure services such as Azure Data Factory, ADLS, Key Vault, Azure Functions, Azure Batch service, Azure Machine Learning and related cloud infrastructure.
- Knowledge of Databricks concepts including clusters, policies, Delta Tables and performance optimisation techniques.
- Strong understanding of Snowflake data warehouse concepts and operational best practices.
- Exposure to workflow orchestration technologies such as Control M and associated operational practices.
- Experience with CI/CD and DevOps processes, including source control and deployment pipelines using technologies such as Git and Azure.
- Working knowledge of programming and query languages such as Python,R and SQL; • Experience supporting production deployments, code migrations and changes within cloud-based data environments.
- Demonstrated experience in incident management, problem management and root cause analysis.
- Strong problem-solving, analytical and debugging skills.
- Experience using service and collaboration tools such as Jira, Jira Service Desk and Confluence.
- Demonstrated experience identifying and implementing operational or process improvements.
- Experience working across Engineering, Development, Operations and business teams to resolve technical issues and deliver outcomes. 3.

Commercial

Skills & Qualifications • Tertiary qualification in Information Technology, Computer Science or a related discipline is preferred.





- Process-oriented approach with strong attention to detail and a commitment to operational quality.
- Good understanding of ITIL principles and service management practices.
- Understanding of IT incident, change and problem management processes.
- Ability to engage effectively with both Business and IT teams.
- Strong communication and stakeholder management skills.
- Customer-centric approach with the ability to understand operational impacts and prioritise service outcomes.
- Ability to communicate technical issues, risks and resolutions clearly to technical and non-technical stakeholders.
- Ability to work collaboratively across cross-functional teams and coordinate activities through to resolution.
- Sound understanding of enterprise IT systems, integrations and dependencies is advantageous.
- Ability to balance incident resolution, operational priorities and continuous improvement activities.
- Relevant Azure, Snowflake or other cloud technology certifications are highly regarded. 4.

Additional Responsibilities Area of Accountability : Cloud Workload Management Key Responsibilities :

- Maintain cloud-based applications • Manage cloud environments in accordance with company security guidelines.
- Deploy and debug cloud components as needed in accordance and engage external vendors when required.

Performance

Measures • Cloud Data Framework maintained to a high standard and operated within SLA.

Area of Accountability : Incident Management Key Responsibilities :

- Maintains incident repository and manages key information such as Incident & problem fixes procedures, dependencies, and notification lists • Resolve incidents raised on applications.

Performance

Measures • Measurable continuous improvement in quality and productivity demonstrated Area of Accountability : Problem Management Key Responsibilities :

- Manage risks and resolves issues that affect production systems • Measure and monitor progress to ensure application fixes are delivered on time and within budget, and that they meet or exceed expectations • Weekly Incident Reporting Performance Measures • Contribution to the process improvements.

Service

Improvements.

📌 AIML Ops Support Engineer (Melbourne)
🏢 Accenture
📍 Melbourne