You’ll be working within product delivery teams and will collaborate with architecture, cyber security, data governance and platform engineering teams as well as being a key influencer with business stakeholders. 

This position is Australia-based, with Sydney, Melbourne or Brisbane preferred and may require occasional travel to various Downer sites.

About the Role

In this role, you will:

• Design, build, test and deploy enterprise grade AI solutions and agentic workflows across MS Azure and Databricks ecosystems.
• Using manual and spec-driven techniques, develop clean, testable and well-documented Python, C# and SQL code that meets engineering standards and supports long-term supportability.
• Develop integrated PDLC and DDLC agent workflows which delivery software applications, data pipelines and products.
• Build AI solutions using frontier and open weight models using RAG, VectorDBs, Knowledge Graphs and MCP servers.
• Use Azure AI Foundry, Databricks Mosaic AI and approved AI engineering tools to support solution development and delivery.
• Contribute across the delivery lifecycle, including problem framing, data exploration, prototyping, feature engineering, model evaluation, deployment, monitoring and support.
• Design and maintain MLOps pipelines, including CI/CD for machine learning, automated model training, evaluation gates, deployment and monitoring.
• Manage experiment tracking, model versioning and reproducibility through the Databricks MLflow model registry.
• Implement solution performance monitoring with evaluation agents for drift detection, hallucination, and model performance degradation.
• Apply spec-driven development, DevOps, secure-by-design, responsible AI and human-in-the-loop review practices.
• Contribute to engineering standards, code reviews, prompt reviews, technical design sessions and agile delivery ceremonies.
• Produce and maintain technical documentation, including architecture decision records, runbooks, model cards, data lineage records and pipeline documentation.
• Support and mentor junior engineers through knowledge sharing, pairing and agent-assisted delivery walkthroughs.
• Work with architecture, governance and cyber security teams to ensure solutions meet Downer’s enterprise, security and responsible AI standards.
• Contribute to cost optimisation and FinOps practices across Azure Databricks and Azure AI Foundry.

This is an opportunity to contribute to Downer’s uplift in AI-native delivery while developing your technical depth across modern AI/ML engineering, agentic workflows and governed enterprise AI.

Our Ideal Candidate

You are a hands-on engineer who enjoys transforming complex problems into practical, reliable and maintainable solutions.

You are curious about emerging AI engineering tools, a coder at heart but take pragmatic and responsible approaches to using AI tooling to accelerate development in enterprise environments. You are comfortable collaborating with product, architecture, security, governance and business stakeholders, and can explain technical concepts, limitations and trade-offs clearly to both technical and non-technical audiences.

What You Will Bring

• A tertiary qualification in Computer Science, Computer Engineering, Software Engineering, Information Technology, Data Science, Mathematics, Statistics, quantitative analysis or a related discipline.
• Relevant industry certifications in Azure AI, Databricks, GitHub, software engineering, DevOps, security or cloud data platforms will be highly regarded. 
• At least five years’ total experience across software and/or data engineering or related technical field.
• At least two years relevant hands-on experience developing advanced analytics, machine learning or generative AI solutions for production environments.
• Practical experience with Azure and Databricks ecosystems, including exposure to Azure AI Foundry, Databricks Mosaic and Genie Code.
• Familiarity with model hosting in platforms such as Azure AI Foundry, Databricks Mosaic AI and AWS Bedrock is highly regarded.
• Experience with AI engineering tools such as Claude Code, Codex, Cursor, GitHub Copilot and Copilot Studio is highly regarded. 
• Experience in DevOps engineering, CI/CD, secure-by-design principles and FinOps controls recognising the importance of balancing token-economics with performance.
• Familiarity with orchestration frameworks such as CrewAI, Strands Agents and Microsoft Agent Framework is valued, as is familiarity with spec-driven development, agentic product delivery lifecycle practices and agentic business workflows.
• Strong hands-on capability in Python, C#, SQL, Git-based version control, automated testing, code review and production-quality engineering practices.
• Practical understanding of platform controls implemented through Unity Catalog, Microsoft Purview and Agent 365 as part of an enterprise Data & AI governance ecosystem.

You are known for your curiosity, sound technical judgement and commitment to producing secure, reliable and explainable AI solutions that deliver meaningful business outcomes.

You’ll be working within product delivery teams and will collaborate with architecture, cyber security, data governance and platform engineering teams as well as being a key influencer with business stakeholders. 

This position is Australia-based, with Sydney, Melbourne or Brisbane preferred and may require occasional travel to various Downer sites.

About the Role

In this role, you will:

• Design, build, test and deploy enterprise grade AI solutions and agentic workflows across MS Azure and Databricks ecosystems.
• Using manual and spec-driven techniques, develop clean, testable and well-documented Python, C# and SQL code that meets engineering standards and supports long-term supportability.
• Develop integrated PDLC and DDLC agent workflows which delivery software applications, data pipelines and products.
• Build AI solutions using frontier and open weight models using RAG, VectorDBs, Knowledge Graphs and MCP servers.
• Use Azure AI Foundry, Databricks Mosaic AI and approved AI engineering tools to support solution development and delivery.
• Contribute across the delivery lifecycle, including problem framing, data exploration, prototyping, feature engineering, model evaluation, deployment, monitoring and support.
• Design and maintain MLOps pipelines, including CI/CD for machine learning, automated model training, evaluation gates, deployment and monitoring.
• Manage experiment tracking, model versioning and reproducibility through the Databricks MLflow model registry.
• Implement solution performance monitoring with evaluation agents for drift detection, hallucination, and model performance degradation.
• Apply spec-driven development, DevOps, secure-by-design, responsible AI and human-in-the-loop review practices.
• Contribute to engineering standards, code reviews, prompt reviews, technical design sessions and agile delivery ceremonies.
• Produce and maintain technical documentation, including architecture decision records, runbooks, model cards, data lineage records and pipeline documentation.
• Support and mentor junior engineers through knowledge sharing, pairing and agent-assisted delivery walkthroughs.
• Work with architecture, governance and cyber security teams to ensure solutions meet Downer’s enterprise, security and responsible AI standards.
• Contribute to cost optimisation and FinOps practices across Azure Databricks and Azure AI Foundry.

This is an opportunity to contribute to Downer’s uplift in AI-native delivery while developing your technical depth across modern AI/ML engineering, agentic workflows and governed enterprise AI.

Our Ideal Candidate

You are a hands-on engineer who enjoys transforming complex problems into practical, reliable and maintainable solutions.

You are curious about emerging AI engineering tools, a coder at heart but take pragmatic and responsible approaches to using AI tooling to accelerate development in enterprise environments. You are comfortable collaborating with product, architecture, security, governance and business stakeholders, and can explain technical concepts, limitations and trade-offs clearly to both technical and non-technical audiences.

What You Will Bring

• A tertiary qualification in Computer Science, Computer Engineering, Software Engineering, Information Technology, Data Science, Mathematics, Statistics, quantitative analysis or a related discipline.
• Relevant industry certifications in Azure AI, Databricks, GitHub, software engineering, DevOps, security or cloud data platforms will be highly regarded. 
• At least five years’ total experience across software and/or data engineering or related technical field.
• At least two years relevant hands-on experience developing advanced analytics, machine learning or generative AI solutions for production environments.
• Practical experience with Azure and Databricks ecosystems, including exposure to Azure AI Foundry, Databricks Mosaic and Genie Code.
• Familiarity with model hosting in platforms such as Azure AI Foundry, Databricks Mosaic AI and AWS Bedrock is highly regarded.
• Experience with AI engineering tools such as Claude Code, Codex, Cursor, GitHub Copilot and Copilot Studio is highly regarded. 
• Experience in DevOps engineering, CI/CD, secure-by-design principles and FinOps controls recognising the importance of balancing token-economics with performance.
• Familiarity with orchestration frameworks such as CrewAI, Strands Agents and Microsoft Agent Framework is valued, as is familiarity with spec-driven development, agentic product delivery lifecycle practices and agentic business workflows.
• Strong hands-on capability in Python, C#, SQL, Git-based version control, automated testing, code review and production-quality engineering practices.
• Practical understanding of platform controls implemented through Unity Catalog, Microsoft Purview and Agent 365 as part of an enterprise Data & AI governance ecosystem.

• You are known for your curiosity, sound technical judgement and commitment to producing secure, reliable and explainable AI solutions that deliver meaningful business outcomes.