AI/ML Software Development Team for EU Companies: How to Scale with Expert Talent
AI/ML Software Development Team for EU Companies: How to Scale with Expert Talent
There is no shortage of pressure on mid-sized European companies to build AI capabilities. Boards are asking about it. Competitors are announcing it. And yet the vast majority of organisations are stuck at the same point: they know they need to act, but they do not know what team they actually need to build.
The mistake most companies make is treating this as a hiring problem. They post for a data scientist, maybe an ML engineer, and wait for something to happen. Nothing does. The real challenge is not ambition — it is team design. You do not need a large AI department, and you do not need to replicate what a tech giant does. What you need is a lean, well-structured AI/ML software development team that can identify viable use cases, work with imperfect internal data, ship into your existing systems, and operate within the security and compliance constraints that EU companies face — whether you are running legacy infrastructure or mid-way through a generative AI or wider digital transformation programme.
The core idea here is simple: Mid-sized EU companies do not need the biggest AI/ML team. They need the smallest team that can move a real use case from data to production safely and repeatedly. That is a very different problem to solve, and this article shows you how to solve it.
What an AI/ML development team actually does
Before you think about who to hire, it is worth being precise about what this team actually delivers. A lot of organisations conflate exploration of artificial intelligence with actual AI delivery. They are not the same thing, and confusing the two is one of the main reasons teams stall.
- An AI/ML software development team focused on delivery, rather than research or hype, covers a well-defined set of activities across the full software development lifecycle:
- Identifying viable use cases — assessing which business problems have the right combination of data availability, commercial value, and technical feasibility to justify building.
- Data preparation and governance — cleaning, labelling, structuring, and creating reliable data pipelines. This is the part of the development process that takes the longest and gets talked about the least.
- Model development and validation — building machine learning (ML) models, testing them against real-world edge cases, and iterating until the output meets acceptance criteria, not just benchmark scores.
- Integration into products or workflows — connecting models to existing systems, whether web applications, internal tools, or operational platforms. The decision to integrate AI has to account for what already exists, not just what is technically possible.
- Deployment, monitoring, and iteration — moving through the software development process into production, then maintaining model performance as data evolves and business intent shifts over time.
- Security, privacy, and compliance support — building in GDPR-appropriate data handling, role-based access control, and audit trails from the start, not as an afterthought.
The common thread across all of this is measurable business outcomes. The development workflow should always be traced back to a specific business objective. If it cannot be, the team is probably working on the wrong thing.
Core roles in an AI/ML development team
Getting the team composition right matters more than getting the headcount right. A small team with the correct roles in the right proportions will consistently outperform a larger team that is missing a critical function. Here is a practical breakdown of the roles you need, what they do, and how to staff them at different stages.
| Role | Main responsibility | When do you need them | Type | Key consideration |
|---|---|---|---|---|
| AI product manager / product owner | Translates business objectives into product alignment, manages sprint planning and acceptance criteria, owns the roadmap. | From day one. Without this role, development workflow drifts from business needs. | Full-time | Often the hardest to find. Needs both commercial and technical fluency. |
| Data scientist | Builds and validates machine learning models, analyses data quality, identifies feature importance, manages test scenarios. | When you have a defined use case and enough data to work with. | Full-time | Should be involved in scoping, not just execution. Avoids expensive late-stage pivots. |
| ML engineer | Takes validated models from data scientists and productionises them — handling performance optimisation, reliability, and integration into existing systems." | When you move from experimentation to building something that needs to run in production. | Full-time | The bridge between data science and software engineering. Often underestimated in importance. |
| Data engineer | Builds and maintains the data pipelines that feed your models. Handles ingestion, transformation, storage, and data quality. | Early. Poor data infrastructure causes more AI project failure than poor modelling does. | Full-time or shared | Can sometimes be shared with your existing data function if one exists. |
| MLOps engineer | Manages model deployment, monitoring, retraining pipelines, versioning, and the overall production environment for AI systems. | When you are approaching production readiness or managing multiple models. | Part-time or shared initially | Missing MLOps capability is one of the most common reasons POC success does not translate to production. |
| Backend / software engineer | Integrates AI outputs into your products, builds APIs, and handles the software development work that surrounds the model itself. Software engineers and software developers in this role need enough AI context to work effectively alongside the rest of the team. | When you need the model connected to something real — a web application, an internal tool, or an operational workflow. | Full-time | Often it already exists in your organisation. Needs AI context to work effectively alongside the team. |
| UX or business analyst | Ensures the AI output is usable by the people it is designed for. Maps business intent to user experience and identifies where human judgment is still required — particularly in workflows where automated output influences critical decisions. | Whenever the output surfaces to a human user or needs to influence human decision making. | Part-time or shared | Undervalued in AI teams. Poor UX is a major driver of weak user adoption. |
| Compliance / security stakeholder | Provides oversight on GDPR compliance, data handling, model explainability requirements, and procurement alignment. | From scoping through to launch. Not a sign-off step at the end. | External or shared | Particularly important in sensitive sectors such as finance, healthcare, and insurance. |
For most mid-sized companies, the full roster above does not need to be employed simultaneously. The structure evolves as the work moves from discovery to pilot to production. What matters is that you know which role is missing before you feel the gap.
The best team structure for mid-sized companies
There is no single correct structure for an AI/ML software development team, but there are patterns that consistently work for mid-sized organisations. The right model depends on where you are in the AI journey, how much internal capability you already have, and how quickly you need to deliver.
Small cross-functional squad
The most effective starting point for most companies is a tight squad of four to six people covering the core roles:
- an AI product manager
- a data scientist
- an ML engineer
- a data engineer
- and a backend developer.
This group can take a single use case from scoping through to a working pilot. It is small enough to move quickly and large enough to cover the essential disciplines. The cognitive load stays manageable because everyone is close to the work, and decision making does not require significant time in meetings.
This is the model to use when you are proving out AI delivery for the first time.
Hybrid internal and external team
Once you have a proven use case, many companies find the most effective path is combining a small internal core, typically the product owner and one or two engineers who own the institutional knowledge, with an external partner who provides specialised roles like MLOps, data science, or compliance support.
This structure lets you scale without committing to a large permanent headcount, and it allows the entire team to access skills you would struggle to recruit for directly.
It is also better suited to handling repetitive tasks and accelerating delivery when internal capacity is constrained.
Team evolution from discovery to pilot to production
The most practical framing for a mid-sized company is to think about the team in three phases rather than a single permanent structure:
- During discovery, you need an AI product manager, a data scientist, and a business analyst — people who can assess feasibility and define the business case.
- During the pilot phase, the data engineer and ML engineer join to build something real.
- When you move to production, MLOps and backend engineering become critical, and compliance needs to be embedded rather than consulted.
Allowing the team to evolve this way means you are not carrying cost before you need capability, and you are not scrambling for skills when you need them most.
In-house vs outsourced vs hybrid AI/ML dev teams
This is one of the most common strategic decisions mid-sized EU companies face, and it is rarely as clear-cut as the vendors on either side suggest. The right model depends on a combination of factors: budget, urgency, internal capability, continuity needs, data sensitivity, and the complexity of the production environment you are building into.
| Model | Best for | Strengths | Risks | Typical fit |
|---|---|---|---|---|
| In-house team | Companies with long-term AI ambition, stable budgets, and an existing technical base to build on. | Deep context, continuity, control over the full software development process and data governance. | High cost to recruit and retain. Slow to scale. Risk of building the wrong team before understanding what you need. | Large digital-first businesses or organisations making AI a strategic differentiator over the long term. |
| Fully outsourced | Time-critical proof-of-concept work, or situations where internal capability is very limited. | Speed to start, access to a full team, reduced overhead, no recruitment lag. | Risk of weak knowledge transfer, dependency on the vendor, and poor integration with existing systems if scoping is loose. | Companies that need to test a use case quickly without committing to permanent headcount. |
| Hybrid internal + external | The majority of mid-sized EU companies, particularly those balancing internal continuity with the need for specialist skills. | Flexible, scalable, combines institutional knowledge with external expertise. Allows teams to access deep AI and MLOps skills without hiring permanently. | Requires clear governance and role definition between internal and external teams to avoid overlap or gaps. | The default model for companies that have identified a viable use case and want to move to production without building an entire department. |
The hybrid model tends to work best for mid-sized EU companies precisely because it mirrors how serious AI delivery actually operates: a small, knowledgeable internal core working alongside specialists who bring production experience, MLOps capability, and integration depth. Data sensitivity should drive your decision on what stays internal. Production complexity should drive your decision on what you bring in from outside.
Common reasons AI/ML teams fail
Most AI/ML projects do not fail because of bad modelling. They fail because of structural and organisational problems that were present long before a model was ever trained. Understanding these patterns makes them avoidable.
| Reason | Description | How to overcome it |
|---|---|---|
| Unclear business case | The project begins without a defined business objective or measurable outcome. The development process runs but nobody knows what success looks like. | Define the business intent before any technical work starts. What decision will this improve? What will it cost if it does not work? Tie acceptance criteria to business value, not model metrics. |
| Poor data quality | Teams discover mid-project that the data is incomplete, inconsistent, or not structured in a way that supports the use case. This is the single most common cause of delay. | Run a data audit before committing to a build. Understand what you have, what shape it is in, and whether a data engineer needs to remediate it before modelling can start. |
| No ownership after launch | The team delivers the model, declares success, and moves on. Nobody monitors performance, retrains when data drifts, or handles the incidents that come up in production. | Assign ownership before launch, not after. An MLOps engineer or a named internal owner should have explicit responsibility for the model once it is live. |
| Missing MLOps capability | Only 22% of companies successfully deploy models to production. The gap is usually not the model itself — it is the infrastructure, monitoring, and retraining pipelines that keep it running reliably. | Treat MLOps as a first-class requirement, not a nice-to-have. Build deployment and monitoring into the development workflow from the start. |
| Treating POC success as production readiness | A prototype that performs well in a controlled environment is not the same as a production system. Teams that skip the hardening phase create fragile outputs that break under real-world conditions. | Treat the POC as the end of phase one, not the end of the project. Budget and plan explicitly for integration, testing, performance optimisation, and production hardening. |
| Weak user adoption | The model works, but the people who are supposed to use it do not trust it or do not know how to work with it effectively. Business value never materialises. | Involve end users in scoping and testing. Build in human intervention points where judgment matters. Make explainability a design requirement, not an afterthought. |
| Leaving privacy and compliance too late | GDPR considerations, data minimisation requirements, and explainability obligations get raised at the end of the project, requiring rework that could have been avoided. | Involve compliance and security stakeholders from the scoping phase. In EU markets especially, the cost of retrofitting governance is significantly higher than building it in from the start. |
EU-specific considerations when building an AI team
Operating in the EU adds a layer of complexity that companies in other markets do not face to the same degree. None of this should be treated as a blocker and most of it is entirely manageable if you plan for it from the start. What follows is operational guidance, not legal advice. Every organisation should consult their own legal and compliance teams on specific obligations.
| Consideration | Description | Importance |
|---|---|---|
| GDPR and lawful basis for data usage | Any personal data used to train or run a model must have a lawful basis under GDPR. This includes customer data, employee data, and inferred data. Establish the legal basis before building data pipelines, not after. | High |
| Data minimisation | Models should only use the personal data that is strictly necessary for the stated purpose. This affects how you design data pipelines and feature engineering processes, and requires deliberate choices from data engineers and data scientists. | High |
| Role-based access control | Access to training data, models, and production outputs should be limited by role. This is both a GDPR obligation and a basic security requirement for any team handling sensitive business or customer data. | High |
| Explainability in sensitive sectors | In regulated industries such as financial services, healthcare, insurance or energy, AI-driven decisions may need to be explainable to regulators or to the individuals they affect. Black-box models may not be acceptable, and this should shape your model selection process. | High (sector-dependent) |
| Hosting and vendor decisions | Where data is stored and processed matters. Using US-based cloud providers for personal data processing requires additional safeguards under GDPR. EU-based hosting or providers with adequate transfer mechanisms should be evaluated as part of your architecture decisions. | Medium to high |
| Cross-border data handling | If your team includes engineers in non-EU countries, common in outsourced or hybrid models, data transfer rules apply to how they access training data and production systems. This needs to be addressed in your vendor and staffing agreements. | Medium |
| Internal governance and procurement alignment | Mid-sized EU companies, particularly those mid-way through digital transformation, often have procurement requirements that affect how external AI partners are selected and contracted. Build this into your timeline. | Medium |
How to choose the right AI/ML software development partner
Choosing an external AI/ML development partner is not like buying software. The relationship affects how quickly your team learns, how well your models integrate into existing systems, and whether you end up with something you can maintain or something you are permanently dependent on. The criteria below reflect what actually separates partners who deliver from those who pitch well.
Partner evaluation checklist
- Proven production delivery — can they show you live AI systems they have built, not just prototypes or case studies written at the POC stage?
- Integration experience — have they worked with organisations that have complex legacy infrastructure and messier-than-ideal data? Most mid-sized companies do.
- Legacy system capability — the AI output has to connect to something that already exists. A partner who only builds greenfield systems will struggle.
- Governance and security maturity — do they have documented approaches to GDPR compliance, data handling, and model audit trails? Ask for specifics.
- Transparent staffing model — will you know who is actually on your account? Are they the same people who were presented in the pitch?
- Measurable delivery process — do they define success in business outcomes, or in technical metrics? The former is what drives real value.
- Post-launch support — what happens after go-live? Who owns model monitoring, incident triage, and retraining? Is this included or an additional cost?
- EU market experience — have they worked with companies navigating GDPR, the EU AI Act, or sector-specific regulation? This is not theoretical because it affects how they architect and deliver.
Signs your company is ready to build an AI/ML team
Not every company is at the same point, and building an AI/ML software development team before you are ready is as costly as building one too late. These indicators help you assess your genuine readiness, not just your ambition.
Readiness checklist
- You can name at least one specific business problem that AI could address, not 'we should use AI' but 'we lose significant time to this specific process and there is data that describes it.'
- You have data that is relevant to that problem, it does not need to be perfect, but it needs to exist and be accessible to an engineering team.
- There is executive sponsorship, someone with authority has committed to the use case and will make decisions when they need to be made.
- Your existing systems have documented APIs or accessible integration points. AI cannot deliver business value if it cannot connect to the things your business runs on.
- There is an internal owner willing to take accountability for the output after launch. AI systems need a named human who cares whether they keep working.
- You have a realistic timeline. Production-ready AI typically takes longer than organisations expect, and teams that start with an unrealistic deadline make bad architectural decisions.
- Your data handling is at least partially governed. You understand what personal data you hold, where it lives, and whether you have the right to use it.
- There is budget for the full cycle, not just the build, but the integration, testing, launch, and at least one year of monitoring and iteration.
How AI Engineering helps EU companies build and scale AI/ML teams
Grid Dynamics is a NASDAQ-listed technology consulting and engineering firm with over 18 years of experience delivering AI and AI SDLC services
AI Engineering by Grid Dynamics is a consulting and engineering company that delivers AI development services and AI SDLC services tailored for small and medium-sized EU-based companies looking to scale their digital transformation.
Our AI/ML practice covers the full development lifecycle, including use case discovery and data engineering through MLOps, production deployment, and ongoing iteration. We hold AWS Machine Learning Competency status, reflecting formally validated expertise in building and integrating ML workloads at scale.
What working with AI Engineering by Grid Dynamics looks like
If you are at the scoping stage, we offer a free half-day workshop with senior AI/ML experts, a practical session focused on your specific challenges, covering use case viability, your current data landscape, and what a realistic path to production actually looks like.
Start building your AI/ML team
Reach out todayFor organisations ready to move faster, our machine learning platform starter kit on AWS gets a production-ready, cloud-native ML platform operational within weeks rather than months, allowing teams to reduce costs and accelerate delivery without sacrificing quality or compliance
Our staffing model is transparent. You work with engineering teams who have a deep understanding of both the technical landscape and the specific tasks your business needs to solve, supported by continuous development through Grid University and the backing of our wider engineering community and CTO office. Our engineers do not operate in isolation — they bring the depth of a mature AI practice to your team from day one.
If you are a CTO, Head of Engineering, or Head of Data at a mid-sized EU company trying to work out whether you are ready to build an AI/ML team or which model fits your situation, talk to our team. We have worked across various sectors and have handled the EU-specific complexity that most global vendors underestimate.
Final takeaway
The question for most mid-sized EU companies is not whether to build an AI/ML software development team. It is how to build one that actually ships. That means being precise about roles, honest about your data, deliberate about team structure, and realistic about the gap between a proof of concept and a production system.
You do not need a large team to start. You need the right team — one that can move a real use case through the full software development lifecycle and into production within a realistic timeline, without creating governance or compliance problems on the way. The companies that get this right tend to start smaller than they expected, move faster than they planned, and scale with confidence because they built the foundations properly the first time.
The AI landscape rewards organisations that build with discipline, not just ambition. If you are ready to take the next step, start with a use case you can name, data you can access, and a partner who has shipped AI into production environments like yours before.
FAQ
How many people do you need for an AI project?
For a first AI/ML project, a team of four to six people is typically sufficient. A product owner, a data scientist, an ML engineer, a data engineer, and a backend developer cover the core disciplines. You can add an MLOps engineer as you approach production.
Starting with a small cross-functional squad reduces cognitive load, keeps decision making fast, and makes it much easier to maintain product alignment throughout the development process. Larger does not mean faster, it usually means slower.
Should mid-sized companies build AI teams in-house?
Not necessarily, and not straight away. Most mid-sized EU companies benefit from starting with a hybrid model, a small internal core combined with an experienced external partner who provides the specialist roles that are difficult to recruit for directly.
Building an entirely in-house AI/ML team before you have proved your first use case in production is an expensive way to learn what you actually need. Start lean, prove the model, then decide what to bring in-house based on what you have learned.
What is the difference between a data scientist and an ML engineer?
A data scientist focuses on understanding data, building models, designing test scenarios, and validating that a model performs correctly against the problem it is meant to solve. An ML engineer takes that validated model and makes it production-ready, optimising performance, integrating it into existing systems, and ensuring it can run reliably at scale.
Both roles are necessary, and the confusion between them is one of the reasons teams end up with models that work in development but fail in production. The data scientist explores and validates; the ML engineer productionises and maintains.
When should a company hire an MLOps engineer?
Earlier than most companies think. MLOps capability should be considered when you are moving from a validated proof of concept to a production deployment, which is often the most technically demanding part of the entire project. Without it, you risk deploying a model with no monitoring, no retraining pipeline, and no one who owns what happens when model performance degrades.
Given that only around 22% of companies successfully get models to production, the absence of MLOps is one of the clearest predictors of a project that stalls. If you are using a hybrid model, your external partner should have this capability; you should not rely on it arriving later.
Is a hybrid AI team better than full outsourcing?
For most mid-sized EU companies, yes. Full outsourcing can work well for time-critical proof-of-concept work, but it creates dependencies, often produces weak knowledge transfer, and can result in a team that builds something your engineers cannot maintain or iterate on.
A hybrid model, where an internal product owner or technical lead works alongside an external AI/ML team, preserves institutional knowledge, keeps business intent central to the development workflow, and gives you a clearer path to internalising capability over time. It also tends to produce better outcomes on data sensitivity and governance, because someone inside the organisation has genuine ownership throughout.
