Senior AI Engineer (LLM /GenAI)

Excolo.; Sydney; Hybrid; AUD 160,000 - 200,000; Full time

Job summary

Excolo. in Sydney is seeking a senior AI/ML engineer to shape how we deploy large language models at scale. You'll own features from concept to production, with executive backing and a clear roadmap.

You will build and maintain the infrastructure, tackle cost and latency, and collaborate with product managers and data engineers. This is a hands-on, growth-focused role with mentoring opportunities as the team expands.

Qualifications

• Strong software engineering fundamentals with 5+ years experience, including recent production AI/ML work.
• Hands-on experience building with LLMs: RAG, prompt engineering, fine tuning, model APIs and evals
• Solid Python and experience with cloud platforms (AWS or Azure)
• Experience getting models into production, not just notebooks. You know monitoring, versioning and rollback for AI systems
• Ability to talk trade-offs with non-technical stakeholders and push back when something shouldn’t be built
• Full working rights in Australia

Responsibilities

• Design and ship LLM-powered features into production, including RAG pipelines and evaluation frameworks.
• Build and maintain the infrastructure behind it: vector databases, embedding pipelines, model serving and monitoring.
• Manage cost, latency and quality trade-offs across model providers (OpenAI, Anthropic, open source).
• Write production Python and deploying on AWS or Azure with CI/CD.
• Set engineering standards for how AI gets built across the business, including guardrails and testing.
• Work closely with product managers, data engineers and stakeholders to take ideas from prototype to production.
• Mentor mid-level engineers as the team grows

Skills

LLM development

Python

Model deployment

Cloud platforms

Cost/latency trade-offs

Stakeholder communication

MLOps tooling

Mentoring

Tools

LangChain

LlamaIndex

PyTorch

TensorFlow

MLflow

SageMaker

Azure ML

AWS

Job description

We're working with a globally recognised financial services business looking to build on their AI function. They're well past the experiment stage, with executive backing, a growing engineering team and real budget behind the roadmap. The systems this team builds go into production and get used at serious scale, not parked in a demo environment.

This is a newly created senior role, so you'll have a real say in how things get built rather than inheriting someone else's decisions.

Sydney CBD, hybrid (3 days in office)

$160,000 to $200,000 + super

Permanent, full time

Day to day you'll be:

• Designing and shipping LLM powered features into production, including RAG pipelines, agentic workflows and evaluation frameworks
• Building and maintaining the infrastructure behind it: vector databases, embedding pipelines, model serving and monitoring
• Managing cost, latency and quality trade-offs across model providers (OpenAI, Anthropic, open source)
• Writing production Python and deploying on AWS or Azure with proper CI/CD
• Setting engineering standards for how AI gets built across the business, including guardrails, testing and responsible use
• Working closely with product managers, data engineers and stakeholders to take ideas from prototype to production
• Mentoring mid-level engineers as the team grows

What you'll bring

• Strong software engineering fundamentals with 5+ years experience, including recent production AI or ML work
• Hands-on experience building with LLMs: RAG, prompt engineering, fine tuning, model APIs and evals
• Solid Python and experience with cloud platforms (AWS or Azure)
• Experience getting models into production, not just notebooks. You know what monitoring, versioning and rollback look like for AI systems
• The ability to talk trade-offs with non-technical stakeholders and push back when something shouldn't be built
• Full working rights in Australia

Nice to have

• Experience with LangChain, LlamaIndex or similar frameworks
• Classical ML background (scikit-learn, PyTorch, TensorFlow)
• Exposure to MLOps tooling like MLflow, SageMaker or Azure ML
• Experience in a scale-up or enterprise environment where you've built AI capability from early days