At TL Consulting, we simplify contemporary complexity with advanced, sustainable engineering. As a proudly Australian-owned consultancy with over 15 years of experience, we empower businesses to innovate and achieve measurable outcomes through scalable, cloud-native solutions. Our 50+ consultants operate across key sectors including banking, financial services & insurance (BFSI), government, retail, education and entertainment. This cross-industry expertise allows us to deliver customer-centric solutions that combine deep technical capability with a clear understanding of each client's strategic vision. Our approach leverages proven, scalable blueprints tailored to your business direction - enabling accelerated outcomes, maximised ROI, and sustainable long-term adoption.

We maintain strategic partnerships with organisations that share our vision for a cloud-native future. Through our collaborations with Microsoft, GitHub and Databricks, we ensure our capabilities stay aligned with evolving best practices. These partnerships also provide access to innovation programmes, co-funding opportunities and dedicated support - helping us accelerate time-to-value for our clients.

About the role

The AI Engineer builds and operates AI-powered applications and agentic systems for TL's clients, with a focus on Azure AI Foundry, retrieval-augmented generation, and AI-assisted engineering practice. This is a hands‑on delivery role. You build what the solution design calls for and prove it works: deploying and integrating models, building retrieval and agent components, modernising existing applications and writing the evaluations that demonstrate output is fit to put in front of users.



The ideal candidate is a capable software engineer with production AI experience who moves between client environments and technology stacks, and who is as interested in whether a solution works as in building it.

KEY RESPONSIBILITIES

AI Application & Agentic Engineering

- Deploy and configure models on Azure AI Foundry and equivalent platforms, and build the Terraform and CI/CD that provisions them.
- Integrate models into applications: inference clients, retry and timeout behaviour, structured output, streaming, and token and cost instrumentation.
- Build retrieval‑augmented generation pipelines covering chunking, embeddings, vector and hybrid search, and re‑ranking, and diagnose retrieval failures.
- Build agentic workflows across application runtime, GitHub Actions and Copilot Studio, including recognising when a deterministic workflow is the better answer.
- Implement Model Context Protocol (MCP) servers and tool integration patterns where applicable.

Application Modernisation & AI‑assisted Delivery

- Modernise existing applications full-stack, using AI‑assisted methods to accelerate delivery rather than only adding AI features.
- Work specification‑first: write and review the specification before implementation, and use it to drive coding agents.
- Apply GitHub Copilot, Claude Code and similar tooling within an agreed engineering workflow.

Evaluation & Quality





- Build evaluation harnesses: golden datasets, regression suites, automated output comparison, and LLM‑as‑judge where appropriate.
- Run shadow evaluations comparing a current model or platform against an incumbent, and report what the data shows.
- Tune guardrails and content filters against real workload content and measure the residual false‑positive rate.

Cost, Governance & Handover

- Instrument token usage, latency and cost, and attribute spend so it traces to a team or product.
- Work within the engagement's model and prompt governance: versioning, approval, and evaluation thresholds that gate a release.
- Document solutions so the client's team can operate them after handover, and contribute to internal knowledge sharing.

SKILLS & EXPERIENCE

Education

- Bachelor's degree in Computer Science, Software Engineering, Information Technology, Engineering, Mathematics or a related quantitative discipline.
- Equivalent demonstrable experience will be considered in place of a formal qualification. We assess on what you have built and shipped.
- Relevant certification is welcome but not required, for example Microsoft Azure AI Engineer Associate or Azure Developer Associate.

Experience

- 2 to 4 years in software or platform engineering, including at least one AI or ML system taken to production and supported afterwards.
- Practical retrieval‑augmented generation experience, including the failure modes.
- Experience evaluating model output with a method more rigorous than reviewing a sample of responses.
- Exposure to client‑facing or cross‑team delivery.

Technical Skills

AI & LLM Engineering

- Hands‑on with

📌 AI Engineer (Sydney)
🏢 TL Consulting Group
📍 Sydney