About the Centre for Advanced Data APAC The Centre for Advanced Data (C4AD) is Accenture's specialist centre of excellence that sets the architecture standard for contemporary, AI-ready data estates across APAC, and mobilises the talent to deliver them at scale. The C4AD operates across three core disciplines: Master Data Architecture, Knowledge Engineering, and Data Products. Every practitioner in the C4AD is expected to apply agentic and generative techniques as the default way of working, not as an add-on.

The centre supports large-scale data and AI transformation programmes across the region, ranging from enterprise data modernisation to AI-ready platform builds.

Join the Centre for Advanced Data APAC as an AI Engineer, where you will build and ship production-grade AI systems end-to-end across client engagements. You will design agent orchestration, build RAG pipelines, and develop LLM-based solutions for enterprise-scale deployment within large data modernisation programmes where agentic AI is a core part of the delivery. You will work alongside Data Architects and Knowledge Engineers within the C4AD, taking the architectural direction and ontological foundations they set and turning them into working, production AI systems that perform reliably at enterprise scale.

Project

Description / Key Responsibilities - Build and ship production-grade AI systems end-to-end across client engagements, from initial design through to deployment and operation.

- Design and implement agent orchestration systems, including multi-agent pipelines, tool-calling workflows, ReAct-style agents,



and human-in-the-loop control gates using frameworks such as LangGraph, AutoGen, or CrewAI.
- Build RAG pipelines that connect large language models to enterprise knowledge graphs, vector stores, and structured data sources reliably and at scale.
- Develop LLM-based solutions for enterprise deployment, including structured output generation, semantic search, and knowledge-augmented AI workflows grounded in the ontological foundations defined by Knowledge Engineers.
- Implement evaluation frameworks, guardrails, and observability tooling to ensure production AI systems are reliable, auditable, and safe in enterprise environments.
- Integrate AI capabilities into cloud-native data platforms (GCP, AWS, Azure) using services such as Vertex AI, Azure OpenAI, Amazon Bedrock, or Snowflake Cortex.
- Apply production software engineering practices, CI/CD, testing, version control, containerisation, to every system built and deployed.

Key Skills - 3+ years of software or data engineering experience, with at least 1–2 years of hands-on applied AI and LLM engineering in a production delivery context, shipping systems, not building prototypes.

- Proven deep expertise building agentic AI systems,



multi-agent orchestration, tool-use pipelines, and autonomous workflows with real-world reliability requirements.
- Strong Python engineering skills, including async programming, API integration, and production-quality code standards.
- Hands-on experience with LLM APIs (OpenAI, Anthropic, Google Gemini, AWS Bedrock) and agentic frameworks such as LangChain, LangGraph, AutoGen, or CrewAI.
- Proficiency with vector databases (Pinecone, Weaviate, pgvector, Qdrant) and strong command of RAG architecture patterns, including their failure modes and how to engineer around them.
- Experience with cloud AI/ML platforms: Vertex AI on GCP, Azure OpenAI and Azure ML, or Amazon Bedrock and SageMaker.
- Solid understanding of prompt engineering, structured output techniques, and LLM evaluation methodologies for production systems.
- Strong understanding of enterprise data platform fundamentals, pipelines, APIs, data contracts, and query patterns, sufficient to connect AI reliably to enterprise data.
- Knowledge of knowledge graph integration with LLMs, including GraphRAG and ontology-grounded generation, and the ability to consume ontological foundations provided by Knowledge Engineers.
- Familiarity with AI safety, responsible AI practices, and evaluation frameworks for high-stakes enterprise deployment.
- Industry experience in Financial Services, Government, Health, Energy, or Utilities sectors in APAC is highly regarded.
- Good communication skills and ability to learn quickly.

📌 AI Engineer (Melbourne)
🏢 Accenture
📍 Melbourne