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General Manager - AI PLUS
As a General Manager - AI Plus at TechnologyOne, you'll lead the strategy, growth, and evolution of Plus, our next-generation agentic AI experience for TechnologyOne customers. You'll lead a talented cross-functional team spanning AI, product, engineering, and customer-focused disciplines to deliver an intelligent, intuitive, and commercially successful AI capability that transforms how customers interact with their ERP.
Plus is an agentic AI system built on cutting-edge technology and open standards. It uses large language models to understand user intent, identify the right agents to engage, execute tasks and iterate towards the best outcome. Going beyond simply responding to questions, Plus enables customers to interact with their ERP through natural language while proactively surfacing insights, anticipating needs and completing processes on their behalf.
As a senior leader at TechnologyOne, you'll shape the future of AI through Plus, working closely with customers, industry stakeholders, and senior leaders across the business to define its vision, accelerate adoption, and deliver meaningful customer and commercial outcomes.
What you'll be doing…
- Leading the strategy, vision, and execution for Plus, establishing a clear roadmap that delivers differentiated customer and business outcomes.
- Providing strategic and thought leadership on agentic AI, generative AI and emerging AI technologies, translating technological opportunities into practical enterprise solutions.
- Driving the evolution of Plus as a core part of TechnologyOne's SaaS ERP offering and ensuring AI is embedded into the customer experience in meaningful and valuable ways.
- Overseeing the end-to-end product lifecycle, from strategy, discovery and experimentation through to development, launch, adoption and continuous improvement.
- Working closely with R&D and product teams to ensure Plus is built on a scalable, secure and commercially sustainable technology foundation.
- Anticipating customer, industry and technology trends to identify opportunities, manage risk and maintain TechnologyOne's competitive advantage in AI.
- Championing a customer-first approach, working directly with customers and internal stakeholders to understand their needs and demonstrate how AI can simplify work, improve productivity and deliver better business outcomes.
- Driving adoption and commercial growth of Plus, identifying opportunities to expand its value across TechnologyOne's ERP ecosystem.
- Taking full P&L accountability, optimising resources and managing the business unit budget to deliver sustainable growth and profitability.
- Building strong partnerships across R&D, product, consulting, sales, marketing and customer success to ensure a consistent strategy and experience for customers.
- Building, motivating and developing a high-performing team, creating a culture of innovation, accountability, experimentation and continuous learning.
- Attracting and retaining top AI and technology talent through effective succession planning, capability development and engagement initiatives.
- Driving continuous improvement in systems, processes and ways of working to increase the speed, quality and impact of AI innovation.
Your Talents…
- Proven experience leading AI, generative AI, agentic AI, SaaS, enterprise software or technology products in a senior leadership capacity.
- Strong understanding of artificial intelligence and emerging AI technologies, with the ability to translate complex technical concepts into compelling customer and business outcomes.
- Experience shaping and executing product or business strategy for AI-powered products, platforms or digital experiences.
- Strong commercial acumen, with experience managing budgets, investment decisions and P&L accountability in a technology or SaaS environment.
- Proven ability to lead high-performing, multidisciplinary teams and deliver strategic outcomes in a fast-moving and evolving technology landscape.
- Strong understanding of enterprise software and SaaS, with ERP experience highly regarded.
- Experience working with large language models, AI agents, natural language interfaces or intelligent automation will be highly regarded.
- A strong customer focus, with the ability to understand complex customer problems and translate them into simple, intuitive and valuable AI experiences.
- Exceptional strategic thinking and communication skills, with the ability to establish a compelling vision, influence senior stakeholders and bring people together around a common goal.
- Comfortable operating in an environment of rapid technological change, with a willingness to experiment, learn and continuously adapt.
- A passion for using AI to simplify work, improve productivity and create meaningful business outcomes for customers.
Why Join us?
TechnologyOne is proud to be an Equal Opportunity Employer who values diversity and an inclusive workplace. We aim to recruit a diverse range of people with a diverse range of talents to help us to deliver on our mission to better our community.
We pride ourselves on providing our people with earned recognition through talent mapping and career progression, competitive salaries and having a supportive tribe environment. Our core values and culture unleash the boundless potential of creativity, innovation and the entrepreneurial spirit within our people. When you experience Life at TechnologyOne you'll see that our team enjoys an extensive array of benefits, including foundation charity activities, which all cultivate a vibrant workplace our people are very proud to be involved with.
Benefits…
- Competitive remuneration package
- Access to our industry leading employee share plan
- Salary packaging options
- Up to 10 days of additional leave via our Purchase Leave program
- Amazing Culture: 8 x Australian Business Awards "Employer of Choice" winner
- We love our team events & celebrate diversity
- Flexible hours: we are family-friendly and value life outside of work
- Free gym membership in your region (or, if you are located at HQ, we have a new state of the art Gym onsite, exclusive and available 24/7).
- Additional 2.5 days of leave per year dedicated to volunteering at a charity of your choice
- Free breakfast and coffee on-site
- Accelerate your growth with full access to LinkedIn Learning and a clear career framework that maps your path to success.
Who we are…
TechnologyOne (ASX: TNE) is Australia's largest enterprise software company and one of Australia's top 50 ASX-listed companies, with locations across six countries. We provide a global SaaS ERP solution that transforms business and makes life simple for our customers. Our deeply integrated enterprise SaaS solution is available on any device, anywhere and anytime and is incredibly easy to use. Over 1,200 leading corporations, government agencies, local councils and universities are powered by our software.
For 38 years, we have been providing our customers with enterprise software that evolves and adapts to new and emerging technologies, allowing them to focus on their business and not technology.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At TechnologyOne, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
TechnologyOne AI Hiring
TechnologyOne has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Brisbane, Queensland, Australia.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (106) are outnumbered by mid-level (4,384) and senior (3,343) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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