Table of contents
- What is an AI engineer and what do they do in practice?
- AI engineer vs ML engineer vs MLOps engineer: how do the roles differ?
- Which skills does an AI engineer need?
- GenAI engineer, AI agent engineer and LLMOps: where do the new AI roles come from?
- How can you become an AI engineer and prepare for a career in AI?
- How should companies match AI roles and skills to their projects?
- FAQ: common questions about working as an AI engineer
- What next?
- Sources
An AI engineer designs, builds and deploys complete solutions powered by artificial intelligence. That can mean connecting a model to company data, integrating it with an application, testing the system and making sure it remains secure and reliable once it goes live.
The scope of the role varies from one organisation to another. Some AI engineers work with traditional machine learning. Others focus on large language models (LLMs), retrieval-augmented generation (RAG) or AI agents. This is why it makes more sense to judge an AI engineering vacancy by its responsibilities than by the job title alone.
At MINDBOX, we work with clients every day to identify the IT skills their projects require. Through our IT Talent Network, we often help companies clarify what kind of AI engineer they are actually looking for. This article explains the role and answers the questions we hear most often.
What is an AI engineer and what do they do in practice?
An AI engineer takes an AI solution beyond the demonstration stage and makes it work in a real business environment.
Picture a company that wants to process documents more quickly. The AI engineer starts by finding out where the relevant data is stored and who can access it. They choose a suitable model, connect it to the company’s knowledge base and internal systems, build the application and prepare a set of tests.
After launch, they monitor the quality of the answers, response times, operating costs and errors reported by users.
A project might follow this sequence:
- define the business problem and agree on success criteria;
- identify the data, access rights and integrations the solution needs;
- test a model, RAG system or agent;
- evaluate quality and run security tests;
- deploy the solution with monitoring, fallback mechanisms and rollback options;
- collect feedback from users and make further improvements.
In a generative AI project, the engineer may build a RAG system that retrieves information from approved sources before answering a question. In another project, the job may involve an agent that can use tools or an application that calls a model through an API.
Training a proprietary model from scratch is only one possible scenario. Many companies do not need it at all.
Microsoft describes an AI engineer as someone involved in designing, programming, integrating, deploying, maintaining and monitoring AI solutions. This definition appears in Microsoft Learn’s “Training for AI engineers” and “Study guide for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution”.
Microsoft’s AI-102 study guide also shows how broad the role can be. The exam itself was retired on 30 June 2026, but the guide remains a useful overview of the responsibilities involved.
The title AI developer is sometimes used to mean the same thing as AI engineer. In some companies, however, it refers to a more conventional software development role: building an application that uses AI without taking full responsibility for the model, data and production operations.
AI engineer vs ML engineer vs MLOps engineer: how do the roles differ?
An AI engineer usually looks after the solution as a whole. An ML engineer focuses more closely on the model and training pipeline, while an MLOps engineer makes sure models can be deployed and operated reliably.
These are broad areas of responsibility, not fixed industry definitions.
| Area | AI engineer | ML engineer | MLOps engineer |
|---|---|---|---|
| Main objective | Build an AI solution that performs a specific task within an application or business process | Build, evaluate and improve a model and its ML pipeline | Make model deployment and operation reliable and repeatable |
| Primary focus | The application, AI model or service, data, APIs, integrations and user experience | Training data, features, experiments, models, validation and generalisation | CI/CD/CT, model registries, infrastructure, versioning, monitoring and rollback |
| Typical output | An AI application, copilot, agent, RAG system, NLP or computer vision service, or model integration | A model, training code, data pipeline and experiment metrics | A deployment pipeline, platform, telemetry, release policies and operational runbook |
| Main question | Does the complete solution perform the right task, and can people use it safely? | Does the model perform well on new data and meet the agreed criteria? | Can the solution be reproduced, deployed, monitored and scaled? |
| Typical risk | An impressive demonstration with poor quality, weak integration or insufficient control | A good model with no clear route into a working product | An efficient platform built around a use case that provides little value |
In practice, the boundaries are rarely clear-cut. Google describes a Professional Machine Learning Engineer as someone who may also work with MLOps, generative AI, applications and governance in its “Professional Machine Learning Engineer” certification profile. The current “AWS Certified Machine Learning Engineer: Associate (MLA-C02)” profile covers traditional and foundation models, agents, CI/CD, monitoring and security.
In a small team, one person may handle work from all three columns. Before applying for a role or recruiting for one, ask:
- Which result and parts of the system will this person own?
- Does the company use existing models or develop its own?
- Who is responsible for the data, infrastructure, security and monitoring?
- How does the team measure the quality of the solution after launch?
- Will the person work on production systems, or mainly run experiments?
Which skills does an AI engineer need?
Software engineering is still the strongest foundation for this role. Knowing how to use a language model or write a prompt is not enough when a system has to work with real data, users and business processes.
An AI engineer should be able to write software and integrate systems. In most cases, this means Python or another language used within the organisation, plus experience with APIs, Git, testing, containers and application architecture.
They also need a working knowledge of data and machine learning. That covers SQL, preparing and validating data, choosing metrics, basic modelling and recognising where a model is likely to fail.
Generative AI projects bring in another set of technologies: model APIs, RAG, vector or hybrid search, structured outputs, tool calling and agents.
Evaluation deserves particular attention. Model responses should not be judged solely by whether they look correct. Teams need representative test cases, clear acceptance criteria, regression tests and a way to examine failures.
A new prompt, model or RAG index may improve one use case while making another worse. Without systematic testing, that trade-off is easy to miss.
Once the system reaches production, the engineer also has to think about cloud infrastructure, automated deployments, monitoring, versioning and costs. System availability is only part of the picture. The team should track answer quality, response times, resource use and changes in behaviour after the data or model has been updated.
Security cannot wait until the end of the project. It covers access control, data protection, secrets management, minimum permissions for agents and testing for prompt injection.
OWASP’s “Top 10 for LLM Applications 2025” also identifies risks such as the disclosure of sensitive information, excessive agency, improper output handling, and weaknesses in vectors and embeddings. Any high-risk action, such as making a payment or changing customer data, should require approval from a person.
Technical skills are not enough on their own. The engineer must understand the business process, turn a problem into requirements and KPIs, and agree on acceptance criteria. They also need to explain the system’s limitations to colleagues outside the technical team.
Not every AI engineer needs advanced mathematics, experience in training neural networks or the ability to administer Kubernetes.
When a project uses existing models, coding, data, testing and application architecture usually matter more. If the company develops its own models, the balance shifts towards statistics, mathematics and machine learning.
GenAI engineer, AI agent engineer and LLMOps: where do the new AI roles come from?
Most of the newer titles describe a specialism within the wider AI delivery process. They do not yet form a settled list of separate professions.
A GenAI engineer or LLM engineer builds applications based on large language models. Their work often involves RAG, evaluation, data integration and ways to control the model’s responses.
An AI agent engineer connects a model to tools and business processes. They decide what the agent is allowed to do, which permissions it receives and when a person must approve an action. Much of the job is about integration and reliability, not prompt writing.
An LLMOps engineer looks after the operational side of applications built around large language models. They may manage prompt and configuration versions, evaluation datasets, deployments, quality monitoring, feedback and costs.
Microsoft defines LLMOps as a process that covers the complete lifecycle of an LLM application, from experiments to production. This definition appears in Microsoft Learn’s “LLMOps: Operational management of LLMs”.
An AI platform engineer builds shared services for several teams. Their work may standardise access to models, observability, security controls and cost policies across the organisation.
Larger companies may also create dedicated roles in AI security or AI governance. An AI solution architect makes decisions that span several systems. An AI product manager or process owner takes responsibility for the problem, priorities, business value and adoption.
A company does not need a separate job title for every area. It does, however, need to assign each responsibility to a named person or team.
Prompt engineering remains useful, but it is only one part of the work. A strong prompt cannot correct poor data, unsuitable permissions or missing monitoring. Nor can it replace testing and integration.
How can you become an AI engineer and prepare for a career in AI?
The good news is that there is no single route into AI engineering. The most sensible starting point is usually your current role: use the skills you already have and work on the gaps that matter for the type of AI engineering you want to do.
Backend and software developers already understand programming, APIs and system integration. They often need more experience with models, evaluation and data.
Data scientists know how to run experiments and build models, but may be less familiar with application architecture, deployment and production support.
Data engineers bring experience with pipelines and data quality. DevOps and cloud specialists understand infrastructure and automation, but need to learn how models are developed, assessed and maintained.
From our perspective, a strong portfolio project should show far more than a chatbot that answers a handful of questions. It should explain:
- which problem the system solves and how success is measured;
- where the data comes from, how it is updated and who can access it;
- how the candidate compared models or architectures;
- how the test set was built and which failure scenarios were tested;
- how the solution is deployed, monitored and rolled back;
- how much each correctly completed task costs, rather than the price of one model call;
- which decisions require human approval and who is responsible if something goes wrong.
The same questions can expose a badly defined vacancy. Be cautious if an employer expects one person to take care of AI strategy, data, model training, architecture, security, infrastructure, compliance and user adoption.
A certification can help someone organise their knowledge of a particular technology. It cannot replace hands-on project experience. A strong candidate should be able to justify the model they chose, talk openly about failures and explain what they changed when the first approach did not work.
How should companies match AI roles and skills to their projects?
The starting point is the use case. Connecting an existing LLM to a company knowledge base and internal systems calls for a different mix of skills from building a proprietary predictive model.
An application based on an LLM will usually need AI or GenAI engineering, backend development, data expertise and someone who owns the business process.
A proprietary predictive model places more weight on ML engineers, data scientists and data engineers. If the organisation operates many models and releases new versions regularly, it will need stronger MLOps capabilities or support from a platform team.
Agent-based systems also need tightly managed permissions, action monitoring, audit trails and clear human-in-the-loop rules.
A company does not necessarily need to hire a large permanent AI team from the start. An external IT partner can support a short pilot, as long as the arrangement includes knowledge transfer and clearly assigned responsibilities.
Knowledge of the company’s own data, processes, risks and success criteria is best kept inside the organisation.
When deciding whether to build, buy or work with a partner, ask:
- Does the solution create an advantage because of the company’s own data or processes?
- Will the company still need these skills after the pilot has finished?
- Who will take responsibility for maintenance, costs and risk after launch?
The budget is not limited to a specialist’s salary or the price of an API. The full cost covers data, integrations, licences, cloud services, evaluation, monitoring, security, user support and ongoing maintenance.
The cost of each successfully completed case is often a more useful measure than the cost of the tokens used to process it.
The final step is to assign ownership. Who is responsible for the data? Who owns model quality? Who looks after integrations and security? Who takes responsibility for the system once it is running?
The answers will tell you whether the project needs one broadly skilled AI engineer, a specialist ML or MLOps engineer, or several people with complementary roles.
If you are unsure which type of AI specialist your project needs, talk to us.
Grzegorz Bartołd
Senior Key Account Manager
FAQ: common questions about working as an AI engineer
Does an AI engineer need to know how to code?
Yes. It is an engineering role, so programming, APIs, data, testing and application architecture are part of the job. The exact balance depends on the project, but knowing how to use AI tools is rarely enough.
Does an AI engineer train models from scratch?
Not always. Many projects use existing models, leaving the AI engineer to focus on connecting them to data, applications and business processes. Training proprietary models matters more in certain specialist roles.
Does an AI engineer need advanced mathematics?
It depends on the specialism. Building proprietary models requires a solid understanding of statistics and mathematics. In applications based on existing models, software engineering, data, evaluation, cloud infrastructure and security may be more important.
Can a data scientist become an AI engineer?
Yes. Data scientists already have relevant experience with data, models and experiments. They will usually need to add application architecture, integration, deployment and production maintenance to their existing skills.
What is the difference between MLOps and LLMOps?
MLOps brings structure and repeatability to the development, deployment and maintenance of machine learning systems.
LLMOps applies similar principles to applications based on large language models. It also covers prompt and context versioning, retrieval assessment, response evaluation, and control over token use and costs.
Both aim to create a production process that teams can repeat, monitor and improve.
Is prompt engineer a separate profession?
Dedicated prompt engineering jobs do exist, but prompt writing alone rarely covers full responsibility for an AI solution.
In many teams, prompt engineering is one of the skills used by an AI engineer, software developer, researcher, analyst or evaluation specialist.
What next?
If you are building a career in AI, take a look at MINDBOX’s current projects and vacancies. Focus on roles where the responsibilities fit your experience and the direction in which you want to develop.
If you are building an AI solution, start with the use case, data, integrations and responsibility for production. MINDBOX can then help you find the right specialist or team for the current stage of the project.
Sources
Microsoft Learn, “Training for AI engineers”:
https://learn.microsoft.com/en-us/training/career-paths/ai-engineer
Microsoft Learn, “Study guide for Exam AI-102: Designing and Implementing a Microsoft Azure AI Solution”:
https://learn.microsoft.com/en-us/credentials/certifications/resources/study-guides/ai-102
Google Cloud, “Professional Machine Learning Engineer”:
https://cloud.google.com/learn/certification/machine-learning-engineer
Amazon Web Services, “AWS Certified Machine Learning Engineer: Associate (MLA-C02)”:
https://docs.aws.amazon.com/aws-certification/latest/machine-learning-engineer-associate-02/machine-learning-engineer-associate-02.html
Google Cloud Architecture Center, “MLOps: Continuous delivery and automation pipelines in machine learning”:
https://docs.cloud.google.com/architecture/mlops-continuous-delivery-and-automation-pipelines-in-machine-learning
Microsoft Learn, “LLMOps: Operational management of LLMs”:
https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/mlops-in-openai/
OWASP GenAI Security Project, “Top 10 for LLM Applications 2025”:
https://genai.owasp.org/llm-top-10/
