Lead Data Scientist (AI/ML)

Nuage Technology Group; Melbourne; On-site; AUD 150,000 - 190,000; Full time

Job summary

Nuage Technology Group in Melbourne seeks a Lead Data Scientist (AI/ML) to drive technical direction for ML/GenAI initiatives. This senior IC role combines hands-on engineering with leadership across teams, emphasizing scalable AI systems, feature engineering and production deployment.

You will mentor engineers, shape standards and collaborate with business leaders to translate complex ML challenges into practical outcomes.

Qualifications

• 8+ years across Data Science, ML and AI engineering.
• Productionising ML systems and GenAI apps.
• Strong Python software development fundamentals.
• Experience with Databricks for ML workflows.
• Cloud experience with AWS and microservices.
• CI/CD, testing, monitoring and production support.
• Mentoring engineers and setting engineering standards.

Responsibilities

• Lead solution design across ML and GenAI teams.
• Design and improve production ML platforms.
• Build and scale LLM applications and RAG pipelines.
• Establish reusable patterns across Python, APIs, testing, CI/CD.
• Improve reliability and observability of AI/ML services.
• Mentor engineers and influence decisions.
• Collaborate with managers and business leaders on priorities.

Skills

Python engineering

ML lifecycle

GenAI & LLMs

Cloud architecture

CI/CD

MLOps

REST APIs

Docker

LangChain

Tools

Databricks

LangChain

LlamaIndex

Docker

Job description

Lead Data Scientist (AI/ML)

Melbourne

Join a leading consumer technology business as a Data & AI Tech Lead, helping shape how machine learning and Generative AI solutions are designed, built and scaled.

We're looking for someone who has evolved from Data Science into ML Engineering and more recently AI Engineering, combining strong ML foundations with hands-on software engineering and experience delivering production AI solutions.

This is a senior individual contributor role with no direct people management. You'll influence technical direction across multiple teams, establish engineering standards and mentor engineers while remaining close to the technology.

What you'll do

• Technical Leadership: Lead solution design across ML and GenAI teams, influencing architecture, engineering standards and technical roadmaps.
• ML Engineering: Design and improve production ML platforms, including feature engineering, feature stores, model deployment, monitoring and lifecycle management.
• Generative AI: Build and scale LLM applications, RAG pipelines, AI agents and intelligent automation solutions.
• Engineering Excellence: Establish reusable patterns and best practices across Python development, APIs, testing, CI/CD and cloud architecture.
• Platform Ownership: Improve the reliability, scalability, observability and operational readiness of AI/ML services.
• Mentorship: Guide engineers across teams, sharing technical knowledge and improving engineering practices.
• Stakeholder Engagement: Partner with Engineering Managers, Architects and business leaders to influence technical decisions and priorities.

What we're looking for

Technical Experience

• Career Background: 8+ years across Data Science, Machine Learning and AI Engineering, ideally progressing from building predictive models to productionising ML systems and delivering modern GenAI applications.
• Python Engineering: Strong software development fundamentals, including clean code, design patterns, application architecture and scalable backend services.
• Machine Learning & MLOps: Deep understanding of the ML lifecycle, feature engineering, feature stores, model training, deployment and monitoring.
• Databricks: Hands-on experience with Databricks for data processing, ML workflows and model lifecycle management.
• GenAI & LLMs: Practical experience building production applications using RAG, LLM orchestration, LangChain, LlamaIndex or similar frameworks, including evaluation and observability.
• Cloud & Architecture: AWS, Docker, microservices, REST APIs and event-driven architectures.
• DevOps: Familiarity with CI/CD, automated testing, monitoring, logging and production support.
• AI Development Tools: Comfortable using AI coding assistants to improve engineering productivity.

Leadership & Approach

• Proven experience leading technical design and influencing engineering decisions across teams.
• Strong understanding of both traditional ML and modern Generative AI systems.
• Able to translate complex technical challenges into practical business outcomes.
• Enjoys mentoring engineers and improving technical standards.
• Hands-on, pragmatic and comfortable balancing architecture with delivery.

Why apply?

• Work across traditional ML, MLOps and Generative AI, rather than being limited to one area.
• Influence technical direction and engineering standards across multiple teams.
• Build AI products used at scale within an established consumer business.
• Remain hands-on while operating at a senior technical leadership level.
• Join a collaborative engineering environment with strong technical peers.