AI/ML Engineer

Brainfish is looking for two full-time AI/ML Engineers to join our team with strong foundations in machine learning, data engineering, and AI systems design. The AI/ML roles are focused on designing and delivering ML-powered systems-from data preparation through to real-world deployment. You'll play a key role in shaping our intelligent product features-owning everything from model experimentation to deployment. You’ll work with cross-functional teams to bring cutting-edge research into production and contribute to our long-term AI strategy..

Benefits

Salary range: $90,000 - $120,000 per Annum + Superannuation.

Task & responsibilities

• Clean, aggregate, and prepare structured and unstructured data for machine learning tasks.
• Research and implement suitable machine learning algorithms, including deep learning and transformer models.
• Build ML applications that align with specific project requirements, leveraging state-of-the-art techniques.
• Design and run model experiments to test performance, accuracy, and scalability.
• Fine-tune large language models using PEFT methods and optimise RAG pipelines for performance.
• Collaborate with engineering and product teams to translate feature requirements into ML solutions.
• Monitor deployed systems for latency, drift, and performance regressions using modern observability tools..

Qualification & experience

Minimum Requirements:

• Bachelor’s degree in Computer Science, Mathematics, Data Science, or equivalent practical experience.
• 2+ years experience building and deploying machine learning models in production.
• Solid experience cleaning, aggregating, and preparing large datasets for analysis.
• Experience working with structured and unstructured data, including images, text, and video.
• Ability to translate business requirements into machine learning solutions.

Preferred Qualifications:

• Experience fine-tuning LLMs (e.g. LLaMA, Mistral, Deepseek) using LoRA, QLoRA, or other PEFT techniques.
• Hands-on experience with retrieval-augmented generation (RAG) pipelines and mitigating model drift.
• Familiarity with OpenTelemetry, Signoz, or equivalent tools for monitoring model latency and performance.
• Experience managing ML workloads on Kubernetes and building scalable infrastructure.
• Comfortable working across teams to support ML-driven product features..