About DiUS

DiUS is an Australian B2B technology services company with an excellent reputation as a trusted consulting partner. We're a go-to company for helping organisations of all sizes tackle hard engineering problems, improve delivery practices, modernise applications, and deliver or scale new products to market.

Whether we're building a product using AI or building an AI experience, we work on problems our clients aren't able to solve themselves - drawing on our deep range of capabilities across software engineering, experience design, data and analytics, machine learning and AI.

About the role

You'll lead client engagements end-to-end - from shaping requirements and defining proof-of-concepts through to designing, building, training and deploying production ML and GenAI systems in the cloud. You'll get hands-on with everything from classical ML models to agentic and RAG-based GenAI solutions, while also mentoring engineers, managing a backlog, and showing clients what's possible.

You'll also help DiUS keep winning great ML/AI work - supporting pre-sales conversations, scoping solutions, and sharing thought leadership with clients and the wider practice.

What you'll bring

• Commercial experience designing, building and deploying machine learning models and systems - proficiency across at least two of NLP, computer vision, recommendation systems or structured data prediction, with hands-on MLOps and production deployment experience (AWS SageMaker, GCP Vertex AI, Azure ML)
• Commercial experience designing, building and deploying GenAI, LLM, RAG or agentic systems - not just integrating with a hosted API or no-code agent platform. Comfortable across foundation model providers (Anthropic Claude, OpenAI, Gemini, open-weight models), agentic frameworks (Bedrock AgentCore, LangChain/LangGraph, CrewAI, AutoGen), MCP, vector databases, and GenAI evaluation/observability tooling
• Strong Python skills with a solid foundation in software development
• Strong cloud engineering experience and knowledge
• Data engineering exposure - big data tooling, data modelling, and platforms like Databricks or Snowflake, is a strong plus
• The consulting chops to match: comfort with ambiguity, polished communication style, strong stakeholder management, pre-sales experience, and the ability to quickly judge which AI/ML approach actually fits a client's problem
• A genuine passion for staying on top of a fast-moving AI landscape and separating what's useful from what's hype

Why join us

About DiUS

DiUS is an Australian B2B technology services company with an excellent reputation as a trusted consulting partner. We're a go-to company for helping organisations of all sizes tackle hard engineering problems, improve delivery practices, modernise applications, and deliver or scale new products to market.

Whether we're building a product using AI or building an AI experience, we work on problems our clients aren't able to solve themselves - drawing on our deep range of capabilities across software engineering, experience design, data and analytics, machine learning and AI.

About the role

You'll lead client engagements end-to-end - from shaping requirements and defining proof-of-concepts through to designing, building, training and deploying production ML and GenAI systems in the cloud. You'll get hands-on with everything from classical ML models to agentic and RAG-based GenAI solutions, while also mentoring engineers, managing a backlog, and showing clients what's possible.

You'll also help DiUS keep winning great ML/AI work - supporting pre-sales conversations, scoping solutions, and sharing thought leadership with clients and the wider practice.

What you'll bring

• Commercial experience designing, building and deploying machine learning models and systems - proficiency across at least two of NLP, computer vision, recommendation systems or structured data prediction, with hands-on MLOps and production deployment experience (AWS SageMaker, GCP Vertex AI, Azure ML)
• Commercial experience designing, building and deploying GenAI, LLM, RAG or agentic systems - not just integrating with a hosted API or no-code agent platform. Comfortable across foundation model providers (Anthropic Claude, OpenAI, Gemini, open-weight models), agentic frameworks (Bedrock AgentCore, LangChain/LangGraph, CrewAI, AutoGen), MCP, vector databases, and GenAI evaluation/observability tooling
• Strong Python skills with a solid foundation in software development
• Strong cloud engineering experience and knowledge
• Data engineering exposure - big data tooling, data modelling, and platforms like Databricks or Snowflake, is a strong plus
• The consulting chops to match: comfort with ambiguity, polished communication style, strong stakeholder management, pre-sales experience, and the ability to quickly judge which AI/ML approach actually fits a client's problem
• A genuine passion for staying on top of a fast-moving AI landscape and separating what's useful from what's hype

Why join us