Generative AI & Data Science Consultant

Sia Partners; Sydney; On-site; AUD 120,000 - 180,000; Full time

Job summary

Sia Partners is hiring in Sydney's AI & Data unit to transform client processes using Artificial Intelligence, Generative AI and Data Science. You will collaborate with Data Scientists, ML Engineers, and Platform Engineers to design, build and deploy AI solutions that create measurable business value.

Responsibilities span GenAI foundations, retrieval-augmented systems, agentic AI, and enterprise integration, with a focus on governance, performance, and responsible AI practices.

Qualifications

• Master's degree in computer science, AI, information processing, econometrics or related field.
• At least 1 year of technical experience in Data Science, Big Data, or building AI solutions.

Responsibilities

• Assist in designing scalable and secure AI solutions.
• Develop applications powered by foundation models (self-managed or API-based).
• Design prompting and orchestration strategies to improve reasoning and user experience.
• Design and optimize Retrieval-Augmented Generation (RAG) systems.
• Develop AI agents capable of tool usage, orchestration, planning, and human-in-the-loop interactions.
• Architect AI solutions integrating enterprise systems, APIs, databases, and knowledge repositories.
• Evaluate models based on business requirements, performance, latency and cost; monitor production performance.

Skills

Python programming

GenAI frameworks

LangChain

LangGraph

RAG pipelines

Cloud platforms

API development

Education

Master's degree in computer science / AI / engineering

Tools

PyTorch

TensorFlow

Flask

FastAPI

Azure

AWS

GCP

Job description

Sia Partners is hiring in Sydney's AI & Data unit to transform client processes using Artificial Intelligence, Generative AI and Data Science. You will collaborate with Data Scientists, ML Engineers, and Platform Engineers to design, build and deploy AI solutions that create measurable business value.

Responsibilities span GenAI foundations, retrieval-augmented systems, agentic AI, and enterprise integration, with a focus on governance, performance, and responsible AI practices.