AI Engineer

XPT Software Australia Pty Ltd Contract

Sydney, Melbourne, Australia Posted on 09/22/2026

• XPT SoftwareAustralia PTY Ltd, incorporated in 2016, is a Software Services company
  
• XPT works with topclients across Australia in Banking, Insurance, Telecom,Retail, Energy, Mining and Manufacturingdomains.
  
• We have 120+technocrats in Australia working at our clientlocations.
  
• XPT SoftwareAustralia is part of group companies which has globalpresence across India & Europe.
  
• We have served100+ clients globally, fulfilling their onsite-offshoreneeds.
  

Build the AI/agentic layer on top of the SAST/SCAcapability . GitLab's own Duo/agentic features are out of scope for thisprogramme, so this role is responsible for designing and building customautomation using OpenAI (or equivalent LLM APIs) and agentic patterns to reducetriage effort, cut false-positive noise, and speed up remediation

Designand build integrations that pull SAST/SCA findings out of GitLab (viaAPI/webhooks) into an external pipeline for AI-assisted processing.

BuildLLM-based triage assistance: false-positive likelihood scoring, vulnerabilityexplanation in plain language, and contextual remediation suggestions generatedagainst the actual code diff/repo context.

Designagentic workflows (multi-step, tool-using LLM agents) that can, for example,correlate a dependency CVE with actual usage in code, check for available safeupgrade paths, and draft a remediation MR for human review.

Ownprompt engineering, evaluation, and guardrails for these agents - includinghallucination checks, human-in-the-loop approval gates before anyauto-generated fix reaches a merge request, and audit logging for everyAI-assisted decision.

Selectand integrate the LLM provider/stack (OpenAI API, or alternatives) factoring indata residency and confidentiality constraints - telco source code andvulnerability data leaving the environment via a third-party API is agovernance concern requiring explicit sign-off.

Buildfeedback loops so agent outputs (false-positive calls, fix suggestions) are measuredagainst actual analyst/developer decisions to improve accuracy over time.

Workwith the SME to ensure the AI layer complements rather than duplicates nativeGitLab scanning logic, and with the BA to translate desired outcomes (reducedMTTR, lower false-positive rate) into technical automation targets.

Documentarchitecture, data flows, and model usage for security/compliance review -critical in a regulated telco environment where any AI system touching sourcecode or vulnerability data will likely need a formal risk assessment.

Senior, 8+ years software/AI engineering experience,with at least 1-2 years hands-on building production LLM-based or agenticsystems. AppSec domain experience is a strong plus.

Hands-onexperience with LLM APIs (OpenAI, Anthropic, or equivalent) - functioncalling/tool use, structured outputs, and context management at productionscale.

Practicalexperience designing agentic systems - multi-step reasoning, tool-useorchestration, and building in human-approval checkpoints rather than fullyautonomous action.

Strongsoftware engineering skills - able to build and maintain integration pipelines(GitLab API/webhooks, CI/CD hooks) rather than just prototyping in a notebook.

Understandingof prompt engineering best practices and evaluation methodology (how to measurewhether an AI triage decision is actually good, not just plausible-sounding).

Workingknowledge of AppSec concepts (SAST/SCA findings, CVE/CWE, vulnerabilityseverity) sufficient to build meaningful automation.

Awarenessof data governance/privacy implications of sending code and vulnerability datato third-party LLM APIs, and experience implementing controls (redaction,on-prem/private deployment options, data retention policies).

Familiaritywith GitLab's API and webhook model for building external integrations.

Experiencewith self-hosted/open-weight LLMs as an alternative to external APIs, for dataresidency-sensitive use cases.

Priorexperience building AI tooling specifically in a security or DevSecOps context(vulnerability triage, code review automation).

Exposureto evaluation frameworks/observability tooling for LLM systems in production(tracing, guardrail testing, drift monitoring).