Senior AI Engineer - Federal Government New

National Workforce · Canberra ACT 2600 · Full time
Posted 6d ago

Senior AI Engineer - Federal Government New

National Workforce
~$120k - $150k(Estimated)
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KEY POINTS WE FOUND
  • Design, build and deliver AI and machine learning solutions.
  • Work end-to-end across the AI lifecycle from experimentation to production deployment.
  • Implement AI security, privacy, data protection and responsible AI controls.

  • Location: Canberra, Melbourne, Brisbane, Adelaide or Hobart
  • Security Clearance: Baseline (mandatory)
  • Duration: 12 months +

Senior AI Engineer - Federal Government / 12-Month Contract + Extensions
Locations: Brisbane, Canberra, Melbourne, Adelaide or Hobart
Work Arrangement: Hybrid / onsite
Contract: 12 months + extensions up to 24 months
Clearance: Must have a current Baseline

About the Role

A major federal government agency is seeking a Senior AI Engineer to design, build and deliver modern AI and machine learning solutions across a large, complex technology environment.

You'll work end-to-end across the AI lifecycle from experimentation and prototyping through to production deployment, monitoring, optimisation and retirement. The role spans machine learning, generative AI, LLMs, RAG, intelligent automation and AI-enabled decision support.

This is a hands-on engineering role suited to someone who enjoys solving complex problems, building scalable systems and delivering measurable business outcomes.

Key Responsibilities

  • Design, develop and implement AI/ML solutions aligned to business needs.
  • Build and integrate generative AI and LLM-based applications (prompt engineering, RAG, agents).
  • Develop custom AI agents including tool definitions, control flow, memory and escalation paths.
  • Prototype emerging AI technologies to assess feasibility and value.
  • Prepare and engineer data for model development and deployment.
  • Build inference pipelines, orchestration layers and API integrations.
  • Deliver production‑grade solutions using version control, testing, CI/CD and automation.
  • Implement model evaluation covering accuracy, robustness, bias, hallucination and safety.
  • Develop monitoring for performance, drift, latency, cost and business outcomes.
  • Diagnose and resolve issues affecting reliability, security and user experience.
  • Implement controls for AI security, privacy, data protection and responsible AI.
  • Contribute to assurance, governance and risk assessment activities.
  • Document architectures, models, datasets, prompts, dependencies and operational requirements.

Essential Criteria

Programming / Software Development SFIA Level 3 Ability to design, code, test, document and refactor moderately complex programs and scripts using agreed standards and tools. Able to identify issues, propose practical solutions and collaborate effectively with others.

Desirable Skills

  • SAS, Teradata, Python, Cloudera Machine Learning (CML), Red Hat OpenShift
  • AI/ML engineering, statistical modelling and analytical techniques
  • Data cleansing, validation and feature engineering
  • Experience with data science platforms and scalable analytical environments
  • Practical knowledge of LLMs, embeddings, RAG, agents and generative AI patterns
  • AI solution architecture and enterprise integration
  • MLOps / LLMOps, CI/CD, deployment, monitoring and lifecycle management
  • Advanced degree in computer science, statistics, mathematics or related field
  • 3+ years AI/ML engineering experience with a proven track record

If the above sounds like you, please send us your resume by hitting the APPLY NOW

Consultant

jtrevethick@nwtechnology.com.au

Reference number: 195884
Profession:Information & Communication TechnologyInformation & Communication Technology

Company: National Workforce
Date posted: 16th Oct, 2026

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Skills

0 of 20 matched
AdaptableAi agentsAi security and privacyAi/ml solutionsApi integrationCollaboration and communicationCritical thinkingData governance and risk assessmentData preparationGenerative aiLarge language models (llms)Model deploymentMonitoring ai modelsProblem solvingPrompt engineeringPythonRag (retrieval-augmented generation)TeamworkTesting and ci/cdVersion control

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