Staff Engineer - ML/AI

Nuage Technology Group — Australia · Posted ~3 hours ago

Senior Full-time Hybrid

Skills

Machine learning MLOps Generative AI Feature stores Model lifecycle management Drift detection Monitoring Observability LLM pipelines Agentic workflows AI guardrails Technical leadership LLM Feature Stores

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Summary ✨ AI‑Generated

A permanent hybrid Staff Engineer role focused on ML, MLOps, and Generative AI. You will build production AI capabilities, improve model lifecycle and observability systems, design LLM pipelines and agentic workflows, and influence engineering standards and technical direction across teams.

Highlights

Hands-on Staff Engineer opportunity with technical leadership across ML, MLOps, and Generative AI, while remaining an individual contributor and working across multiple engineering teams.

Description

Staff Engineer - ML/AI Permanent | Melbourne | Hybrid We’re working with a major product company looking for a hands-on Staff Engineer to provide technical leadership across its growing ML, MLOps and Generative AI capability. This is a senior individual contributor role with no people management. You’ll work across different engineering teams, helping set technical direction while remaining close to the technology and delivery. What you’ll be working on Building and evolving MLOps capabilities across feature stores, model lifecycle, drift detection, monitoring and observability. Designing production GenAI applications, LLM pipelines, agentic workflows and guardrails. Setting engineering patterns and helping shape the technical roadmap across ML and AI. Providing technical leadership and influencing senior and Principal Engineers. Working on established AI products already delivering measurable improvements to customer and internal workflows. What we’re looking for You’ll ideally come from either a software engineering background with strong ML/AI experience, or a data science/ML background that has evolved into production engineering. We’re particularly interested in experience across: Production ML and MLOps. GenAI, LLM workflows and AI agents. Strong software engineering fundamentals. Model monitoring, observability and lifecycle management. Databricks, AWS, SageMaker, Snowflake, Azure or similar platforms. Technical leadership without moving away from hands-on engineering. The environment is primarily Databricks and AWS, although exact tool matching isn’t essential. Strong engineering and ML/AI fundamentals matter more. This would suit someone who wants to remain deeply technical while having genuine ownership over how ML and AI solutions are designed and delivered. Apply now - jennifer@nuagetg.io