Company: DataAIT Technologies

Location: On-site – Canberra, ACT

Engagement Type: Contract – 9 Months + 2 x 12-Month Extension Options

Security Clearance: Active NV1 Clearance – Mandatory

Primary Discipline: Systems and Software Engineering – Test, Evaluation, Verification and Validation (TEVV)

About DataAIT Technologies:

DataAIT Technologies is an Australian-based boutique Data & AI consulting firm delivering end-to-end capabilities across data architecture, artificial intelligence, AI engineering, governance, assurance, and advanced analytics. We partner with enterprise and government organisations to design, deploy, govern, and assure safe, responsible, and high-performing AI-enabled capabilities.

Position Summary:

DataAIT Technologies is seeking an experienced AI Governance, Assurance and TEVV Specialist to provide technical leadership in the establishment and maturation of Test, Evaluation, Verification and Validation (TEVV) capabilities for complex AI-enabled systems.

The successful candidate will lead the development of AI assurance frameworks, specialised testing methodologies, evaluation approaches, and governance artefacts. The role will involve delivering independent, evidence-based assessments of AI-enabled technologies to support senior decision-makers and enable the safe, responsible, and effective adoption of AI within mission-critical environments.

This role requires a strong combination of AI/ML technical expertise, systems and software engineering experience, assurance capability, and the ability to communicate complex technical findings to senior stakeholders.

Key Responsibilities:

AI Assurance Capability Development and TEVV Strategy

- Lead the design, implementation, execution, and continuous maturation of AI Test, Evaluation, Verification and Validation (TEVV) frameworks.
- Develop testing, benchmarking, verification, and validation methodologies appropriate for AI-enabled systems operating within complex enterprise and networked environments.
- Establish repeatable and automated testing protocols, benchmarks, test harnesses, and validation workflows.
- Develop evaluation methodologies for machine learning models, computer vision, natural language processing, generative AI, and decision-support algorithms.
- Define measurable assurance criteria, performance thresholds, acceptance criteria, and evidence requirements for AI-enabled capabilities.
- Contribute to the integration of AI assurance activities across the system and software development lifecycle.

AI Goverance, Assurance and Compliance

- Develop and implement AI governance structures, risk assessment methodologies, assurance frameworks, and supporting documentation.
- Establish approaches for assessing AI systems against organisational policy, responsible AI principles, safety requirements,



and relevant national and international standards.
- Develop risk matrices and assurance artefacts addressing issues including model performance, reliability, robustness, bias, explainability, traceability, and human oversight.
- Assess AI-enabled systems against applicable cybersecurity, operational safety, data protection, and regulatory requirements.
- Provide advice regarding export control and technology compliance considerations, including ITAR-related requirements where applicable.

Independent Technical Evaluation

- Conduct rigorous, evidence-based evaluations of AI models, algorithms, datasets, and integrated hardware and software systems.
- Assess model performance, robustness, reliability, repeatability, and operational suitability.
- Identify and assess risks including data drift, model drift, algorithmic bias, adversarial vulnerabilities, performance degradation, unexpected behaviour, and system failure modes.
- Evaluate the effectiveness of AI controls, testing processes, monitoring capabilities, and assurance mechanisms.
- Review evidence generated through testing and evaluation activities and determine whether systems meet defined acceptance and assurance criteria.
- Prepare technical evaluation reports, risk assessments, assurance findings, and executive briefings.

Stakeholder Engagement and Technical Leadership

- Act as a subject matter expert across multidisciplinary teams comprising systems engineers, software engineers, AI specialists, domain experts, research organisations, government stakeholders, and industry partners.
- Provide technical leadership and guidance on AI assurance, governance, testing, evaluation, verification, and validation activities.
- Translate complex AI engineering principles and evaluation findings into clear, concise, and actionable advice for senior decision-makers and non-technical stakeholders.
- Contribute to workshops, technical reviews, assurance boards, governance forums, and stakeholder briefings.
- Support the development of organisational capability and best practice in AI assurance and TEVV.

Required Qualifications and Experience Education & Security Clearance

- Active Negative Vetting Level 1 (NV1) security clearance is mandatory.
- Bachelor’s degree or higher in Computer Science, Data Science, Artificial Intelligence, Software Engineering, Systems Engineering, Mathematics, or another relevant quantitative or engineering discipline.





Relevant professional certifications are highly regarded, including:
- Systems Engineering certifications
- Software Testing or Quality Assurance certifications, such as ISTQB Advanced or Expert
- AI Safety, AI Governance, or Responsible AI certifications
- Risk, governance, or assurance certifications such as CGEIT or CRISC
- Relevant systems assurance, cybersecurity, or engineering qualifications

Expertise Desired: AI and Machine Learning Engineering Assurance

Demonstrated experience in evaluating AI or machine learning models, algorithms, and AI-enabled systems, including the development and application of testing, verification, validation, and assurance methodologies across the system or software development lifecycle.

AI Governance and Assurance Framework Development

Demonstrated experience developing, implementing, or contributing to operational frameworks for AI governance, AI safety, responsible AI, risk management, assurance, or technical evaluation.

Systems and Software Engineering

Strong experience in systems engineering, software engineering, systems integration, testing, evaluation, verification, and validation within complex, high-reliability, mission-critical, or enterprise environments.

Evidence-Based Technical Assessment

Demonstrated ability to conduct structured technical assessments, analyse evidence, identify risks and failure modes, and produce defensible findings and recommendations.

Stakeholder Communication

Exceptional written and verbal communication skills, with demonstrated ability to communicate complex technical concepts, risks, and evaluation findings to senior management, technical specialists, and external stakeholders.

Regulatory and Compliance Awareness

Working knowledge of relevant regulatory, cybersecurity, data protection, export control, sovereign capability, or assurance requirements. Familiarity with ITAR-related environments is highly desirable.

Automated Verification and Evaluation

Experience designing or implementing automated testing suites, test harnesses, model evaluation pipelines, observability and monitoring capabilities, CI/CD or CI/CT pipelines, and MLOps practices.

AI Operational Monitoring

Experience with model monitoring, data and model drift detection, performance monitoring, incident management, or continuous assurance for deployed AI systems.

Candidates submitting an application must provide:

1. An up-to-date CV highlighting relevant experience in systems engineering, software engineering, AI/ML, assurance, governance, testing, evaluation, verification, and validation.
2. A Statement of Applicability demonstrating how the candidate's experience, skills, and qualifications directly address the mandatory and highly desirable selection criteria.
3. Contact details for two qualified referees

📌 AI Transformation Lead (Canberra)
🏢 DataAIT Technologies
📍 Canberra