Senior Engineer – ML & AI Systems

Xero

. Design, build and own production systems behind Xero’s AI features .

Posted 11/3/2026full-timeSydney • AustraliaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and operating production-grade backend systems, particularly for LLM and agentic workloads, while ensuring reliability, cost efficiency, and scalability. Proficient in Python and cloud infrastructure, with a strong focus on mentoring and setting engineering standards across AI products.

Highest-signal resume keywords

Production-Grade Backend Systems DesignLLM Workloads ManagementPython ProgrammingAWS Cloud InfrastructureCI/CD and Observability

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

System Design FundamentalsAPIs and Service DesignSQLLarge-Scale Data ProcessingML OperationalizationInference ServingEvaluation PipelinesDistributed ProcessingData Orchestration ToolsCoding in Go, Java, Kotlin, or TypeScript

Soft Skills

Excellent Communication SkillsCoaching MindsetMentoring Ability

Tools & Technologies

MLflowPyTorchTensorFlowSparkDask

Industry Keywords

AI SystemsGenerative AIAgentic SystemsIncident ResponseOn-Call Support

Tech Stack

Tools & technologies

AWSCloudJavaKotlinPythonPyTorchSparkSQLTensorflowTypeScriptGo

About the role

Key responsibilities & impact

Design, build and own production systems behind Xero’s AI features
Build and operate services, pipelines and infrastructure powering LLM-driven and agentic experiences for millions of small businesses
Make architecture decisions shaping scale, cost, reliability and delivery speed
Build and operate platforms, services and practices supporting traditional ML, generative AI and agentic systems in production
Partner with Applied Scientists to build interfaces and harnesses that take models into production
Own engineering and infrastructure for ML and AI systems, including reliability, cost and scale
Contribute to JAX, Xero’s conversational GenAI assistant, and its context-aware and agentic capabilities
Develop intelligent financial workflows such as Automated Bank Reconciliation and smart document capture
Build XeroForce, an AI-powered workflow platform for planning and executing complex tasks across Xero products
Integrate AI into customer-facing experiences and product workflows
Set engineering standards, mentor engineers and improve engineering capability across AI Products
Partner across Xero to make data usable and systems dependable
Participate in on-call and incident response

Requirements

What you’ll need

Track record of designing, shipping and operating production-grade backend systems at scale
Experience building and operating production systems for LLM or agentic workloads, including orchestration, tool use, evaluation, guardrails, latency and cost management
Strong system design fundamentals
Excellent coding in Python or another language suited to web-scale services, including Go, Java, Kotlin or TypeScript
Experience with APIs and service design
Experience with cloud infrastructure, ideally AWS
Experience with CI/CD and observability
Focus on latency, cost, reliability and SLOs
Experience running services in production, including on-call and incident response
Solid SQL and experience with large-scale data processing
Excellent communication skills for explaining complex technical concepts to business and technical audiences
Coaching mindset and ability to set engineering standards and mentor other engineers
Nice to have: experience operationalizing ML or LLM workloads, including inference serving and evaluation pipelines
Nice to have: familiarity with MLflow, PyTorch or TensorFlow, and data orchestration tools
Nice to have: distributed processing experience with Spark or Dask
Successful candidates must complete relevant background checks, which may include credit and criminal history checks
Candidates need working rights for Australia or appropriate visa assistance for the country this role is based in

Benefits

Comp & perks

Flexible hybrid working model combining office collaboration with remote work
Access to modern office spaces
Collaborative 'boost days'
Professional development through mentoring and engineering capability building
Flexible working options
Supportive recruitment accommodations and adjustments
Background-check process and consideration for future opportunities