What does an AI engineer do? Tasks for your first hire
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Morten Laufer
Founder
Most articles on this topic are aimed at job applicants. Here is the hiring perspective: what an AI engineer actually does in mid-sized companies, what sets them apart from an ML engineer, and when making that first permanent hire is worth it instead of commissioning a project.
An AI Engineer integrates existing models (LLM integration, APIs, data pipelines) into applications and operations; dedicated model training usually falls to the ML Engineer or Data Scientist.
Only 5 per cent of companies specifically hire AI specialists, while 51 per cent cite a lack of staff as an AI barrier: SMEs are competing for a scarce supply.
The proportion of AI job adverts has stagnated at around 1.5 per cent since 2022, as a study by IW Cologne and the Bertelsmann Stiftung shows.
Permanent employment is worthwhile for recurring use cases, data access and operational responsibility; for one-off projects, project staffing is often sufficient.
Nova Search fills AI developer roles on both a permanent and project basis: shortlist in 5 working days, 90-day guarantee.
What does an AI Engineer do in a company?
Search results for this question typically provide job descriptions aimed at applicants. For you as a hiring decision-maker, a different answer matters: What does this role actually take on within your team? The AI Engineer translates AI into applications that create genuine business value, operating right at the intersection of data science, software engineering, and business. In medium-sized businesses, the role is almost always focused on integration and operations, not research. Confusing this during the briefing means searching for the wrong profile.
Three core responsibilities from prototype to operations
The largest component is integration: connecting existing models like GPT or BERT to apps, platforms, and workflows via APIs, alongside building the necessary interfaces, backend architectures, and data pipelines. The second component is operations: production-ready AI systems must run reliably, securely, and at scale, encompassing deployment and monitoring. The third component is advisory: business departments need to know what AI can deliver within their processes and where its limits lie—whether for churn reduction, process automation, or risk assessment.
Model integration: Connecting LLMs and APIs to existing products and workflows
Data handling: Connecting and cleaning sources, building pipelines, quality and bias checks
Operations: Deployment, monitoring, scaling, and security in production environments
Advisory: Guiding departments on the opportunities and limitations of AI, prototyping, and feasibility studies
For your briefing, this means: define the use case, not the hype. A role intended to connect a chatbot to an ordering system requires a different focus than one integrating forecasting models into core operations. And because specialisations like ML, NLP/LLM, computer vision, and MLOps are typically only separated in larger organisations, a single person in your team will usually cover integration, operations, and advisory together.
How does an AI Engineer differ from an ML Engineer?
Job adverts in the AI space often mean very different things: sometimes they focus on developing AI, other times on software engineering with an AI focus. If you need someone to design, train, and optimise proprietary models, you are looking for an ML Engineer or a Data Scientist. If you need someone to integrate existing models into apps, platforms, and workflows by building interfaces and APIs, you are looking for an AI Engineer. This exact distinction serves as your guide for shaping the job profile:
Criterion | AI Engineer | ML Engineer | Data Scientist |
|---|---|---|---|
Core Task | Integrating existing models (LLM integration, RAG, APIs) | Training and optimising custom models | Analysing data, scoping use cases |
Focus | Integration, deployment, operations | Modelling, feature engineering, model quality | Statistics, experimentation, feasibility |
Typical Question | How do we stably integrate the model into our product? | Which model best solves this problem? | Is this problem even worth solving? |
In smaller teams, these roles often overlap; in larger companies, they are strictly separated. That is why it pays to review job adverts carefully and define your own role precisely. If you only require integration, avoid listing model training requirements—doing so needlessly narrows your candidate pool without adding value to the role. Conversely, a profile lacking deep ML expertise will not support a true data science use case. For a detailed breakdown of the ML Engineer profile, explore our ML Engineer Guide.
Which skills belong in the job profile?
The job profile determines whether you receive relevant applications or just CV noise. Therefore, strictly distinguish between 'must-haves' and 'nice-to-haves': must-haves are skills without which the hire cannot succeed in year one. Nice-to-haves depend on the specific use case and can be explored during interviews. A profile that labels every skill as essential often leads to a year-long search and an empty pipeline.
Must-haves and nice-to-haves for your job spec
Must-have: Python as the primary language for integration and data pipelines
Must-have: API development and integration, such as REST
Must-have: Cloud experience with AWS, Azure, or GCP
Must-have: Containers and deployment, e.g. Docker and Kubernetes
Must-have: SQL and data pipeline development
Must-have: Strong understanding of operations: monitoring, scaling, and security
Nice-to-have: TensorFlow or PyTorch for custom model components
Nice-to-have: MLOps tooling such as MLflow for versioning and monitoring
Nice-to-have: NLP and LLM integration, such as RAG over proprietary documents
Nice-to-have: Computer vision, if images or video processing are involved
Take soft skills seriously: in medium-sized businesses, this individual operates right at the interface with the business units. They must be able to explain AI models to non-technical stakeholders, present findings clearly, and train teams. This is precisely what you should evaluate in the culture-fit interview. Regarding the market benchmark: as of 2026, a Senior AI Engineer commands a gross base salary of €95,000 to €130,000 p.a. (excluding variable components) in the German market. We cover further details in our dedicated salary guide.
When is a permanent hire worthwhile?
A permanent hire makes sense when AI is intended for product features or core operational processes and three specific criteria are met. If any of these are missing, contract staffing is often the more pragmatic approach before risking an unsuccessful permanent hire:
Recurring use cases: The application is recurring or scalable, rather than a one-off experiment
Direct data access: Data is hosted in-house, backed by proper governance and quality standards
Operational ownership: Someone takes long-term responsibility for monitoring, iteration, and downtime risks
For one-off projects or when operational readiness is still lacking, engaging talent via freelance and contract staffing is often the smarter route. A contractor builds the prototype; after a quarter, you evaluate whether the use case proves viable, and only then make a permanent hire. This sequence protects both your budget and time-to-hire.
Market conditions do not make the decision any easier: currently, only 5 per cent of all companies in Germany are actively hiring specialists with AI skills, while 51 per cent cite a lack of personnel resources as a major hurdle to AI adoption (Bitkom Survey 2025). According to a study by IW Köln and the Bertelsmann Stiftung (2025), the share of AI job adverts has stagnated at around 1.5 per cent of all postings since 2022. Consequently, mid-sized companies are competing for a scarce pool of AI talent. To learn how to secure talent despite these shortages in 2026, read our strategy guide on Finding IT Talent in 2026. And because your employer brand may not yet be widely known among candidates, your pitch is critical: find out what truly resonates with tech professionals in our article on Employer Branding for Mid-Sized Businesses.
If your organisation meets all three criteria, permanent recruitment is the ideal route: Nova Search delivers a shortlist of 3 vetted candidates in 5 working days, evaluated through a two-stage screening process combining in-depth technical vetting and a culture interview—all backed by our 90-day guarantee. 5 days instead of 5 months, with founder-led guidance and no handoffs to juniors. Discover what our permanent recruitment covers in detail and how the 60-minute briefing works on our Permanent Recruitment page.
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FAQ
What exactly does an AI Engineer do in a company?
They translate AI into applications that deliver real value: they integrate models such as GPT or BERT into apps, platforms, and workflows via APIs, build the necessary interfaces, backend structures, and data pipelines, and ensure that systems run stably, scalably, and securely. In SMEs, the role is typically an integration and operations function at the interface between data science, software development, and business—not a research position.
What is the difference between an AI Engineer and an ML Engineer?
An AI Engineer utilises existing models and integrates them into corporate IT; an ML Engineer trains, implements, and optimises custom machine learning models for production use. By contrast, Data Scientists focus more on analysis and conceptual design. In smaller teams, these roles often overlap, whereas larger organisations keep them separate. If you only require integration, you should avoid overloading the job specification with model-training requirements.
Which skills should be included in the job specification for a first hire?
Must-haves: Python, APIs, cloud platforms (AWS, Azure, GCP), containers, deployment, SQL, and data pipelines. Depending on the use case, additional requirements include: TensorFlow or PyTorch, MLOps tooling such as MLflow, NLP/LLM integration, or computer vision. Just as crucial in SMEs: the ability to explain AI models clearly to non-technical business departments.
When is a permanent hire better than project-based staffing?
A permanent hire makes sense when AI is to be embedded into recurring processes or core products, where the individual takes on data access and operational responsibility, and long-term expertise needs to be built in-house. For one-off projects or where operational maturity is still lacking, project-based staffing with freelancers is often the more pragmatic solution before risking a failed permanent hire.
How difficult is it to find an AI Engineer for an SME?
Fairly difficult. Only 5 per cent of all companies actively hire dedicated AI specialists, whilst 51 per cent cite a lack of personnel as a major barrier to adopting AI. According to the IW Cologne and the Bertelsmann Stiftung, the proportion of AI job adverts has stagnated at around 1.5 per cent since 2022. SMEs with a lesser-known employer brand therefore need a compelling pitch.
How long does a placement take with Nova Search?
In tech and AI recruitment, Nova Search delivers a longlist of 20 candidates within 24 hours and a shortlist of 3 qualified candidates within 5 working days, curated through a two-stage screening process comprising an in-depth technical assessment and a culture interview. This is backed by a 90-day guarantee: if the placed candidate leaves the company within 3 months, a replacement is provided at no extra cost.
