How to Build an AI Development Team: Roles, Structure, and Costs
16 min
· May 19, 2023
AI is moving from experimentation to real-world applications, and companies need the right teams to turn its potential into business results. Stanford HAI’s 2026 AI Index Report found that global private AI investment reached $344.7 billion in 2025, up 127.5% year over year.
But building AI products takes more than hiring machine learning engineers. Companies need the right mix of AI, data, software, and infrastructure specialists who can move a project from an initial idea to a reliable production system. A well-designed AI development team brings these skills together, helping businesses develop AI-powered products, integrate existing models, and scale their solutions.
In this guide, we explain how to build an AI team, which roles to include, how to choose the right AI team structure, and what to consider when comparing in-house hiring with a dedicated team.
An AI development team is a group of specialists who design, build, integrate, deploy, and maintain artificial intelligence solutions. Depending on the project, an artificial intelligence team may include machine learning engineers, data scientists, software developers, data engineers, product managers, and infrastructure specialists.
Unlike a data science team that primarily analyzes data, tests hypotheses, and develops models, an AI software development team focuses on turning AI capabilities into usable applications and integrating them into business workflows. This includes data preparation, application development, model deployment, monitoring, and ongoing optimization.
The exact AI team structure depends on the product, available data, technical complexity, and business goals. A small team validating an idea will need a different mix of skills from a company building a large-scale AI platform.
Building an AI development team makes sense when your business has clear AI goals and needs the expertise to turn them into practical solutions. Depending on your requirements, you can hire new specialists, upskill your existing developers, or partner with an external AI development team.
If you have an existing AI integration
Building an artificial intelligence team can help augment your current AI integration with expanded capabilities. You can recruit domain-specific experts to develop new AI models, optimize existing ones, and ensure smooth integration with core business workflows and systems.
When developing AI-driven products
If your business is creating an AI-powered product, you need specialists who can turn an idea into a working application. An AI software development team can cover the full development lifecycle, from validating a use case and building a proof of concept to launching and maintaining the product.
However, you might want to consider distributed teams if you want the developers to work around the clock and reduce the time to market of your AI solutions. You’ll gain an upper edge with this approach, especially if you know how to manage distributed teams.
When automating processes
Another instance when recruiting a team of artificial intelligence developers makes business sense is when implementing automation. Talents with a combined technical skillset can design and implement advanced AI algorithms and systems to significantly reduce human effort and streamline processes.
When enhancing AI scalability and innovation
A prototype that works in a test environment may not perform reliably under real-world workloads. Scaling requires expertise in infrastructure, monitoring, security, and performance optimization.
A mature AI ML development team can improve model performance, manage infrastructure costs, and maintain reliable services as usage grows.
If you want to implement data-driven decision making
A recent study by Forbes reveals that 43% of business leaders rely on data to make daily decisions. AI developers can help you extract meaningful insights from substantial amounts of data for prudent decision-making. They have the technical know-how to implement predictive analytics or machine learning techniques to highlight patterns and associations between data sets.
Building an effective AI development team requires a mix of technical and business expertise. The exact roles depend on your product, data infrastructure, and project goals. Some specialists are essential from the start, while others can join as the project grows.
| Role | Responsibilities | Key skills | When to hire |
|---|---|---|---|
| AI/ML Engineer | Develops, trains, integrates, and optimizes machine learning models. | Python, PyTorch, TensorFlow, model evaluation, ML algorithms | When building and deploying custom AI models. |
| Data Engineer | Builds data pipelines and prepares reliable data for AI systems. | SQL, Python, ETL/ELT, data warehouses, cloud platforms | When AI solutions depend on large or complex datasets. |
| Data Scientist | Analyzes data, tests hypotheses, and develops predictive models. | Statistics, Python, SQL, experimentation, machine learning | When you need advanced analytics, forecasting, or experimentation. |
| AI Research Scientist | Explores new AI methods and adapts advanced models to specific problems. | Deep learning, mathematics, research methods, model architectures | When developing novel AI capabilities or working on complex research problems. |
| MLOps Engineer | Automates model deployment, monitoring, versioning, and retraining. | CI/CD, Docker, Kubernetes, ML pipelines, cloud infrastructure | When deploying models to production and maintaining them at scale. |
| Software Engineer | Integrates AI capabilities into applications, APIs, and existing systems. | Backend or frontend development, APIs, databases, system design | When turning AI models into usable products or business applications. |
| AI Product Manager | Defines product requirements, prioritizes features, and aligns AI development with business goals. | Product strategy, stakeholder management, AI fundamentals, analytics | When coordinating cross-functional teams and connecting technical work to business outcomes. |
| AI Solutions Architect | Designs the overall AI system architecture and selects appropriate technologies. | Cloud architecture, system integration, security, AI infrastructure | When building complex solutions that must integrate with existing systems or scale across the organization. |
An AI product manager defines the product vision, identifies valuable use cases, prioritizes features, and translates business needs into technical requirements. They coordinate stakeholders and help measure whether the solution delivers the expected results.
Business stakeholders represent the needs of departments, end users, and customers. They identify operational challenges, define success criteria, provide domain expertise, and validate whether the AI solution delivers practical value. Chief AI Officer (CAIO) oversees the organization’s AI strategy, identifies opportunities for AI adoption, and aligns AI initiatives with business objectives, governance, and risk management.
A project manager coordinates the development process, manages timelines and risks, allocates resources, and keeps stakeholders informed. In a smaller AI dev team, these responsibilities may be handled by the product manager or a technical lead.
Data scientists analyze complex datasets to identify patterns, test hypotheses, and develop predictive models that address business challenges. They help define data requirements, evaluate the feasibility of AI use cases, and assess model performance before deployment.
A data engineer builds and maintains the infrastructure that makes data available for AI development. Their responsibilities include integrating data sources, ensuring data quality, designing storage systems, and creating reliable data pipelines.
A machine learning engineer develops, tests, optimizes, and deploys machine learning models. They bridge experimentation and production by turning models into software components that can operate reliably within real applications.
Their responsibilities may include model optimization, API integration, performance testing, and preparing models for model deployment. If you’re planning to build a machine learning team, choose the right mix of research and engineering expertise based on your product goals and technical complexity.
An MLOps engineer automates and manages the machine learning lifecycle, from deployment to ongoing monitoring and updates. They build CI/CD pipelines for ML models, manage model versioning, and automate retraining where appropriate.
UX designers are important in the team as they will design the user interface of the AI application to make it as intuitive and accessible as possible.
AI Software Engineers integrate AI capabilities into applications, APIs, and existing systems, ensuring they work reliably within the product architecture. LLM Engineers specialize in building applications powered by large language models (LLMs), such as AI assistants, chatbots, and AI agents.
Share your team requirements, and we’ll launch a ready-to-deliver team in just 2–4 weeks.
The right AI development team structure depends on the maturity and complexity of the solution. Companies can start with a small cross-functional group and expand as the product moves from experimentation to production and scaling.
| Project stage | Typical team size | Key roles | Main priorities |
|---|---|---|---|
| PoC / MVP | 2–5 specialists | AI/ML Engineer, Software Engineer, Data Scientist or Data Engineer, Product Manager (part-time) | Validate the use case, test technical feasibility, and build a working prototype. |
| Production | 5–8 specialists | AI/ML Engineers, Data Engineer, Software Engineer, MLOps Engineer, Product Manager, UX Designer (as needed) | Build a reliable product, integrate AI into existing systems, and ensure security, quality, and deployment readiness. |
| Scaling | 8–15+ specialists | Multiple AI/ML Engineers, Data Engineers, MLOps Engineers, AI Software and LLM Engineers, Product Manager, Project Manager, AI Architect, security specialists (as needed) | Handle growing workloads, improve performance, manage infrastructure costs, and support multiple AI features or products. |
These team sizes are illustrative estimates, not fixed industry standards. Some roles can be combined or engaged part-time, particularly during the PoC/MVP stage. Projects involving advanced AI research, strict regulatory requirements, or large-scale infrastructure may need larger teams.
For companies with several AI initiatives, a centralized team or hub-and-spoke model can help coordinate resources and avoid duplicating work. For a single product, a cross-functional team may provide a more direct route from idea to deployment.
Building an AI development team starts with identifying the right business opportunity and assembling the expertise to turn it into a working solution. From assessing data readiness to recruiting specialists and establishing reliable workflows, each step helps reduce development risks and prepare your AI product for growth.
Start by identifying the business problem the AI solution should address. Define the target users, expected outcomes, available data, technical constraints, and success metrics.
Decide whether the goal is to create a proof of concept, integrate AI into an existing product, or build a production-ready system. Clear requirements help you identify the necessary roles and avoid hiring specialists before the project’s needs are understood.
Before recruiting, evaluate your existing team’s technical capabilities and the data available for AI development. Identify skill gaps in machine learning, software engineering, data infrastructure, cloud computing, and AI deployment. Consider whether current developers can be upskilled to fill some of these gaps.
Data readiness is equally important. Assess data quality, availability, volume, accessibility, and compliance with applicable privacy requirements. Determine whether your data needs cleaning, labeling, integration, or additional infrastructure before it can support model development.
This assessment will help you decide which specialists to recruit, which skills to develop internally, and whether external expertise is necessary.
Consider data quality, availability, security, and ownership. If your organization already has software engineers and data specialists, upskilling them may help fill some gaps. However, advanced model development, deployment, or infrastructure work may require additional AI talent.
Define the roles required to deliver your AI project, based on its complexity and development stage. A small proof of concept may need only an AI/ML engineer and a software developer, with data and product expertise available as needed. A production-grade solution may require dedicated data engineering, MLOps, security, and product management capabilities.
Next, choose the team model that best fits your goals:
Consider your budget, hiring timeline, need for ongoing maintenance, and the strategic importance of AI before making a decision.
Once you have defined the roles and team model, create clear job descriptions that outline responsibilities, technical requirements, and expected outcomes. Evaluate candidates on practical experience, problem-solving, system design, and their ability to deploy and maintain AI solutions—not just theoretical knowledge.
For technical assessment, use role-specific exercises. For example, ask ML engineers to explain model evaluation and deployment decisions, data engineers to design a reliable data pipeline, and LLM engineers to troubleshoot a RAG application or assess model outputs.
Where to find AI specialists:
Regardless of the sourcing channel, evaluate technical skills, communication, collaboration, and experience relevant to your specific use case.
Hiring qualified specialists is only part of building a successful AI team. You also need processes and infrastructure that help them collaborate, deploy models reliably, and manage AI risks.
Establish clear ownership of data, models, APIs, and infrastructure. AI governance should also address privacy, security, access control, potential bias, transparency, human oversight, and applicable compliance requirements.
For distributed teams, establish communication routines, documentation standards, and feedback loops from the beginning. Explore our guide on how to manage a remote development team for practical advice on coordinating remote specialists.
AI technologies evolve quickly, so continuous learning is essential for maintaining an effective development team. Provide access to technical training, certifications, workshops, conferences, and hands-on experimentation with relevant tools and frameworks.
Make sure your team has the resources needed to work effectively, including suitable hardware, cloud infrastructure, software licenses, development environments, and collaboration platforms. Our guide to tools for managing an offshore team can help you choose the right tools for distributed collaboration.
Retention matters just as much as recruitment. Offer opportunities to work on challenging projects, clear career progression, regular feedback, and knowledge-sharing sessions. Encourage engineers to share what they learn and document important technical decisions so expertise remains within the organization.
By investing in both professional development and a supportive team culture, you can build the capabilities needed to maintain and scale your AI solutions over time.
Explore how Newxel helped Renesas to turn a simple staffing request into an R&D center and strategic partnership.
Choosing between an in-house team and a dedicated AI development team depends on your budget, hiring timeline, project complexity, and long-term business goals. In-house teams offer greater direct control and internal knowledge retention, while dedicated teams provide access to specialized talent and more flexible scaling without the need to recruit every specialist yourself.
| Factor | In-House AI Team | Dedicated AI Development Team |
|---|---|---|
| Hiring and setup costs | Recruitment, onboarding, salaries, benefits, equipment, and infrastructure costs. | Usually a service fee covering the agreed team and services; pricing and included costs depend on the provider. |
| Ongoing costs | Salaries, taxes, benefits, training, tools, cloud infrastructure, and retention expenses. | Contracted team fees, plus any separately billed infrastructure, licenses, or additional services. |
| Access to talent | Depends on your recruitment reach, employer brand, and local talent availability. | Access to a broader talent pool and specialists selected for your requirements. |
| Time to build | Recruiting several specialized AI roles can take time, particularly in competitive markets. | Can be faster if the provider already has access to suitable candidates, though timelines depend on availability and project scope. |
| Scalability | Expanding or reducing capacity requires internal hiring or workforce changes. | Team size and skill sets can often be adjusted more flexibly under the contract. |
| Control and collaboration | Direct day-to-day management and close alignment with internal processes. | Shared responsibilities and agreed workflows; the level of control depends on the engagement model. |
| Knowledge retention | Knowledge remains within the organization, provided documentation and employee retention are maintained. | Knowledge transfer and documentation should be explicitly agreed upon to support long-term continuity. |
| Best suited for | Businesses with ongoing AI needs, strategic in-house expertise requirements, and resources to maintain a permanent team. | Businesses that need specialized expertise, faster access to talent, or flexible development capacity. |
An in-house team can be a strong choice when AI is central to your long-term product strategy and you have the time and resources to recruit and retain specialists. A dedicated AI team can help you access skills that are difficult to hire internally or expand development capacity without building every capability from scratch.
Depending on the engagement, businesses may also work with offshore AI developers to access talent across different markets. The key is to evaluate technical quality, communication, security, management practices, and total costs—not just hourly rates.
Explore Newxel’s dedicated development team model and learn more about how to manage an offshore development team.
Building an AI development team involves more than finding engineers with the right technical skills. Common challenges include:
Planning for these challenges early helps businesses build teams that can deliver reliable AI solutions while controlling costs and reducing development risks.
Newxel helps businesses build AI development teams by sourcing and vetting technical specialists to match their project requirements. With access to international talent, companies can fill skill gaps and scale engineering capacity without recruiting every role internally.
Whether you need individual AI engineers, a dedicated team, or a broader R&D setup, Newxel can support your hiring needs. Explore our guide to building an offshore team for AI development to learn more about this approach.
Building an AI development team starts with a clear business objective, realistic requirements, and the right mix of technical and product expertise. As your project evolves from a proof of concept to production and scaling, your team structure, infrastructure, and budget must evolve with it.
Whether you choose in-house hiring, upskilling, or a dedicated external team, focus on data readiness, reliable deployment, governance, and long-term maintenance. A well-planned approach helps you manage costs, reduce hiring risks, and turn AI capabilities into sustainable business value.
