What does an AI engineer really do? Separating myth from reality

AI engineers design and build systems that solve practical problems using artificial intelligence. Their work involves programming, training models, managing data, and integrating AI into existing systems. They collaborate with teams to ensure AI solutions align with business goals, focusing on areas like automation, predictive analytics, and decision-making tools.

Key Points:

• Role Misconceptions: AI engineers aren't creating sentient machines or working in isolation. They focus on practical applications like recommendation systems, fraud detection, and predictive maintenance.
• Daily Tasks: Include coding, testing models, monitoring AI systems, and refining algorithms.
• Skills Needed: Programming (Python, Java), machine learning expertise, data engineering, and strong communication.
• Industries in Australia: AI is transforming healthcare (e.g., diagnostics), mining (e.g., autonomous vehicles), and agriculture (e.g., irrigation optimisation).

AI engineers are essential for businesses adopting AI, with demand rising across Australian industries. They help turn data into actionable insights, driving efficiency and innovation.

What Does an AI Engineer Actually Do?

Main Responsibilities and Skills of AI Engineers

Understanding the daily tasks of AI engineers helps clear up misconceptions about this fast-evolving profession. Their work is hands-on, collaborative, and requires a mix of technical know-how and strong communication abilities.

Primary Job Duties

AI engineers play a crucial role in bridging technical development with business needs. Their primary task is to design, develop, and implement AI systems that address real-world challenges. This involves transforming complex data into self-sufficient AI solutions.

The process usually starts with creating algorithms and building predictive models. AI engineers analyse business goals and translate them into mathematical frameworks that drive these models. Data preparation is another critical step - they clean, organise, and structure data to ensure AI models can learn effectively.

Once the models are ready, engineers focus on deploying them into production environments. This involves integrating AI into existing business systems so it can begin delivering results. They also monitor performance, resolve issues, and ensure the systems continue to function as intended.

AI engineers often work in multidisciplinary teams, collaborating with data scientists, developers, and business analysts to embed AI into business operations, automate processes, and improve decision-making. For instance, during cloud migration projects, they might develop systems to predict delays caused by bandwidth issues or create alerts for migrating non-essential data while enhancing security layers.

"Artificial intelligence isn't a fleeting trend; it's the future of engineering leadership. By harnessing AI's speed and fostering a culture of rapid iteration, your team will outpace competitors, reduce costs, and accelerate innovation."

• Nader Mowlaee, Career Coach

Required Technical Skills

Programming skills are at the heart of AI engineering. Python, used by 77.4% of AI engineers, is a popular choice due to its simplicity and extensive libraries. Java is also widely used for building production-level applications.

Machine learning expertise is another cornerstone of the role. AI engineers need to understand various algorithms, know when to use them, and effectively train models. Strong data engineering skills are equally important - they structure, clean, and prepare data for AI systems, often using big data tools to handle massive datasets.

AI engineers also need experience in deployment and DevOps to transition models from development to production. Security skills are critical as well, ensuring that AI systems are protected from potential threats. Reflecting industry trends, LinkedIn has identified AI engineering as the fastest-growing skill across all sectors for 2025. Additionally, job postings for AI-related roles have doubled in the past two years.

Communication and Teamwork Skills

Technical expertise alone isn’t enough - effective communication is essential for turning AI into practical solutions.

AI engineers must be able to explain complex ideas, present findings, justify decisions, and translate business needs into technical requirements. Their problem-solving skills extend beyond coding, requiring creative thinking to identify challenges AI can address and to develop practical solutions. They also ensure AI outputs align with engineering best practices, making the solutions both reliable and useful.

Teamwork is another defining aspect of the role. Unlike data scientists, who primarily focus on building models, AI engineers are responsible for transforming these models into functional applications. This often involves close collaboration with software developers, business analysts, and operations teams to ensure smooth integration and consistent performance.

An understanding of project management is also valuable. According to Harvard Business Review, only about 35% of business projects are successfully completed. AI engineers who can navigate project timelines, risks, and dependencies become key contributors. Staying updated on new technologies allows them to recommend improvements to existing systems and workflows.

In Australia, these communication and teamwork skills are especially important. AI engineers frequently work in diverse, multicultural teams, requiring them to adapt their communication styles to suit different audiences and business environments.

What AI Engineers Do Each Day

AI engineers juggle a mix of coding, system monitoring, teamwork, and constant learning. Their role isn’t just about writing code in isolation - it’s about blending technical development with system upkeep and cross-department collaboration. These daily routines tie into their bigger responsibility: creating and implementing AI-driven solutions.

Daily Tasks and Activities

Mornings often start with a review of system performance. AI engineers check on deployed models, ensuring they’re functioning as expected. Dashboards are crucial here, offering insights into model accuracy, response times, and any signs of data drift.

A significant part of their day involves designing, coding, and testing AI solutions tailored to specific business needs. This could mean refining algorithms, training models with fresh data, or running experiments to compare different approaches. They also apply natural language processing (NLP) techniques for tasks like building chatbots or analysing documents.

Model evaluation is a continuous process. Engineers frequently perform cross-validation and unit tests, setting up retraining pipelines to keep models performing well. For example, if a recommendation system shows declining click-through rates, they investigate and tweak the model to address the issue.

Data plays a central role in their work, which means collaborating with data engineers to maintain high-quality, real-time pipelines. In banking, this might involve overseeing transactional data flows with tools like Kafka and Spark to support fraud detection systems.

To stay sharp, they also keep an eye on industry trends, exploring new frameworks and tools that could improve their systems.

Tools and Software They Use

AI engineers rely on a robust toolkit to power their daily work. Python remains the go-to programming language, while frameworks like TensorFlow and PyTorch are staples for developing machine learning models.

Cloud platforms are essential for training and deploying these models. For instance, Google Cloud Platform provides scalable infrastructure, while Vertex AI simplifies integrating intelligent systems into workflows. Google Cloud, with over 56,000 user reviews, and Vertex AI, rated by 713 users, are widely recognised for their capabilities.

AI-powered coding assistants are also making a big impact. Sourcegraph's Cody, for example, integrates seamlessly with IDEs like VS Code, Visual Studio, and JetBrains. It offers inline editing and chat features, helping developers save time and boost productivity. Powered by advanced models like Claude Sonnet 4 and GPT-4o, Cody can save developers up to six hours a week and double coding speed.

"Cody, Sourcegraph's AI code assistant goes beyond individual dev productivity, helping enterprises achieve consistency and quality at scale with AI." - Sourcegraph

Version control tools like Git are indispensable for managing code changes and facilitating collaboration. Engineers also use tools for data extraction and organisation to prepare datasets for training.

The growing use of these tools reflects the rapid expansion of AI in Australia. The local AI market is expected to surpass AUD 1.98 billion by 2026, with 65% of Australian businesses already using or planning to adopt AI solutions.

Working with Other Teams

Collaboration is at the heart of an AI engineer’s role. They work closely with data scientists to refine models and introduce new features. These interactions often include sharing insights from system performance and real-world scenarios, which is particularly valuable in areas like e-commerce where prediction accuracy can shift over time.

AI engineers also team up with data engineers to ensure data pipelines are robust enough to handle spikes in demand while keeping systems running smoothly. Beyond technical teams, they work with business units to translate AI capabilities into practical, real-time solutions. This teamwork ensures AI models are not just technically sound but also aligned with business goals, challenging the stereotype of engineers working in isolation.

Common Myths About AI Engineers

There are plenty of misconceptions floating around about what AI engineers actually do. These myths, often influenced by science fiction or outdated ideas, can create confusion for both businesses and job seekers trying to understand the field.

Myths vs Facts

Here’s a breakdown of some common myths about AI engineers and the reality behind them:

These facts highlight the practical and collaborative nature of AI engineering. It’s not about building humanoid robots or creating sentient machines; it’s about developing tools and systems that make life easier and businesses more efficient.

For example, AI engineers play a key role in powering the recommendation engines used by streaming platforms. By analysing user behaviour, they help deliver personalised content, ensuring a better user experience. In manufacturing, they work on integrating IoT sensors with AI systems to enhance precision, cut down on manual interventions, and improve safety measures. Meanwhile, in healthcare, AI is used to analyse medical images, assist in surgeries, and detect anomalies with incredible accuracy, supporting doctors in critical tasks.

Do You Need a PhD to Be an AI Engineer?

The short answer? No, you don’t.

According to LinkedIn's 2024 Emerging Jobs Report, machine learning engineers are among the fastest-growing job roles, and most employers prioritise candidates with undergraduate or master’s degrees over those with PhDs. While having a strong grasp of mathematics, programming, and statistics is essential, these skills can be acquired through bachelor’s or master’s programs, as well as through hands-on experience.

Many AI engineers come from related fields like software development, data science, or machine learning. They often build their expertise through practical projects, online courses, and certifications. Employers are increasingly looking for candidates who can demonstrate their skills through a strong portfolio and real-world experience rather than relying solely on academic credentials.

How AI Engineers Solve Business Problems

AI engineers tackle business challenges by translating broad goals into specific data needs and strategy models. They achieve this by asking critical questions, examining existing solutions, and working closely with stakeholders. Depending on the problem, they decide whether techniques like regression, classification, clustering, or recommendation systems are most suitable. To measure success, they set clear, actionable metrics. This structured approach demonstrates how AI engineers directly influence business outcomes.

AI Engineering in Australian Industries

AI engineering is driving change across Australian industries, addressing challenges in healthcare, mining, and agriculture.

In healthcare, generative AI could contribute around AU$13 billion annually by 2030. For instance, St. Vincent's Health Australia uses AI to predict patient no-shows, streamlining appointment scheduling to cut wait times and improve operations. Elsewhere, Monash Health employs AI to identify patients at risk of deterioration, while The Royal Melbourne Hospital uses AI-based diagnostic tools to assist radiologists in detecting early-stage cancers.

The mining industry is also leveraging AI to improve efficiency and safety. A 2024 GlobalData survey revealed that 96% of employees at major mines globally anticipate AI will significantly impact operations, although 54% cite a lack of proven results as a barrier to further adoption. Fortescue partnered with Liebherr Mining in July 2024 to introduce an autonomous haulage system for zero-emission vehicles at Pilbara iron ore sites, increasing productivity by 30% compared to traditional methods. Similarly, US Steel implemented "MineMind", a generative AI tool, at its Minnesota operations, cutting work order completion times by 20% and reducing maintenance costs.

"conducted a study of the coal mining industry in China, where 3,000 people are killed every year. 80% of these deaths were caused by equipment failure. So, there's a clear connection to a safety use case for predictive maintenance."
– Dirk Claessens, IBM's technology leader for the EU and NATO

In agriculture, AI is transforming farming practices. Farmers in New South Wales use AI to monitor soil moisture and optimise irrigation during droughts. In South Australia, vineyards rely on AI to assess grape health and predict harvest timings, while the Northern Territory employs GPS collars with machine learning to track livestock movements and grazing patterns. Innovations like Ripe Robotics' autonomous robot "Eve", which picks fruit by analysing size, colour, and quality, and Bitwise Agronomy's GreenView, which improves yield forecasting accuracy by 90%, highlight the sector's advancements.

AI engineers evaluate their work through metrics that measure model accuracy, system performance, and business impact. For example, a tech training incubator used generative AI to cut contact centre response times from 24 hours to 6 hours, automate over 80% of queries, and boost customer satisfaction by 13%. These changes translated into potential annual savings of AU$120,000.

These practical applications highlight the growing demand for specialised AI expertise - a gap filled by platforms like Talentblocks.

How Talentblocks Simplifies Hiring AI Engineers

Hiring skilled AI engineers can be daunting, especially when businesses require professionals adept at both technical execution and solving business problems. Talentblocks bridges this gap by connecting Australian companies with pre-validated AI engineers through a user-friendly marketplace.

The platform provides:

• Detailed skill assessments beyond traditional CV screening
• Quick access to pre-vetted AI professionals
• Flexible weekly time blocks tailored to project needs
• Transparent pricing with no hidden costs
• Simplified payment and timesheet approval processes
• Access to a vast network of specialist AI engineers

For businesses aiming to adopt AI, Talentblocks offers tailored hiring recommendations via a consultation wizard. This helps companies avoid common pitfalls, such as hiring generalist developers for specialised tasks. Instead, businesses are matched with experts who excel in critical areas like problem framing, data pipeline design, and deploying models into production environments.

Conclusion: The Truth About AI Engineering

AI engineering is far more grounded and business-oriented than the myths might have you believe. These professionals aren't caught up in science fiction - they're busy building data pipelines, fine-tuning algorithms, and working closely with teams to solve practical challenges. Their focus? Real-world solutions that deliver results.

For Australian businesses, understanding what AI engineers actually do can make all the difference. By seeing their role clearly, companies can make smarter choices about hiring, project planning, and setting realistic expectations. This understanding leads to tangible business benefits. AI engineering is about creating and deploying solutions that are reliable, scalable, and efficient, which directly supports business growth and innovation.

The numbers speak for themselves. McKinsey reports that AI adoption in operations has doubled since 2017, and IBM notes that organisations save an average of USD 1.76 million when they use AI extensively. Plus, 63% of surveyed companies expect to ramp up their AI investments within the next three years.

Take Neural Concept’s collaboration with Airbus in 2019 as an example. Their deep learning application slashed prediction times from an hour to just 30 milliseconds. That’s a game-changing improvement - over 10,000 times faster - allowing for quicker exploration of product designs and significant cost savings. These kinds of breakthroughs highlight just how critical it is to hire the right AI talent for the job.

Traditional hiring methods often slow projects down, but platforms like Talentblocks are changing the game. By connecting businesses with pre-vetted AI engineers, Talentblocks simplifies recruitment. They offer flexible weekly engagement options, clear pricing, and easy payment processes. This ensures companies can quickly onboard experts who not only excel technically but also understand business needs.

What sets Talentblocks apart is its focus on skill validation. Instead of relying on CVs alone, they ensure that candidates are equipped to handle specialised AI tasks, helping businesses avoid the common mistake of hiring generalist developers for roles that demand niche expertise.

As AI continues to reshape industries like healthcare, mining, and agriculture, the demand for skilled AI engineers will only grow. For Australian companies, recognising the real contributions of these professionals - and having access to the right talent at the right time - can unlock AI's potential while steering clear of costly missteps.

FAQs

Which industries in Australia are benefiting the most from AI engineering, and how is it being applied?

In Australia, industries like energy, resources, utilities, and healthcare are at the forefront of using AI engineering to transform their operations. AI is helping these sectors by streamlining processes, boosting safety measures, and enhancing the quality of services. Take mining, for example - companies are now using AI-powered systems to monitor sites in real time, allowing them to spot potential hazards and create safer workplaces.

This progress is made possible through innovations like automating repetitive tasks, data-driven decision-making, and predictive analytics. By embracing AI, businesses in these industries are not only improving efficiency and cutting costs but also sparking new ideas and solutions. AI is proving to be a major force in shaping the future of Australia’s economy.

What skills and qualifications do you need to become an AI engineer, and is a PhD essential?

To thrive as an AI engineer, you'll need to master programming languages like Python, understand machine learning and deep learning models, and be familiar with AI tools and frameworks. A strong foundation in mathematics, data structures, and algorithms is equally important. Beyond technical knowledge, hands-on experience and a portfolio showcasing your projects often carry more weight than formal qualifications.

While a PhD can be advantageous for research-heavy roles, it’s not a requirement for most AI positions. Many professionals excel with just a bachelor's or master's degree, especially when paired with relevant industry experience. In most cases, practical skills and the ability to solve real-world problems take precedence over advanced academic credentials.

How do AI engineers work with other teams to ensure AI solutions meet business goals and integrate smoothly into existing systems?

AI engineers work hand-in-hand with teams like product managers, data scientists, and IT professionals to ensure that AI solutions not only meet business goals but also blend effortlessly into existing systems. Their role involves grasping the organisation's objectives and challenges, then customising AI tools to streamline workflows, automate tasks, and aid in better decision-making.

For seamless integration, AI engineers often collaborate with IT teams to tackle issues like technical compatibility and scalability. At the same time, they engage with stakeholders to ensure the solutions are both practical and effective. In Australia, these efforts are bolstered by programs that promote partnerships between industries and research institutions, driving innovation and supporting AI adoption across various sectors.