Building AI Engineering Teams That Drive Business Value
AI is becoming a core part of how companies build products, solve problems, and stay competitive. As machine learning and data-driven systems move from research into production, organizations are facing new questions about how to hire and structure effective AI teams.
AI engineering hiring is not the same as hiring for traditional software roles. It involves a mix of research, data science, infrastructure, and product thinking. Understanding what makes these teams work is important for long-term success.
This article explains how AI engineering teams create business value. It also outlines which roles matter most, how to structure teams, and how to connect their work to business outcomes.
Why AI Engineering Teams Matter For Business Impact
An AI engineering team builds, deploys, and maintains machine learning and AI systems in a production environment. These teams work across data pipelines, modeling, software infrastructure, and product integration. Unlike traditional software teams, AI teams manage systems that learn from data, not fixed rules.
A well-structured AI team directly affects product velocity, model performance, and business outcomes. When teams are aligned with clear objectives and include the right mix of skills, they are more likely to deliver models that improve over time, integrate with existing systems, and meet user needs.
Examples of companies that have seen measurable gains from effective AI engineering include those that have built recommendation engines, fraud detection systems, or customer support automation. In each case, outcomes were tied not only to the algorithm used, but to how well the engineering team was structured to support the system over its lifecycle.
Key business benefits of well-structured AI engineering teams:
Faster Innovation: Teams with clear ownership, workflows, and collaboration models are able to experiment, iterate, and deploy faster.
Better Problem Solving: Diverse teams with a range of engineering, data, and domain expertise are more effective at solving complex, unstructured problems.
Reduced Technical Debt: Specialized roles, such as MLOps and data engineering, reduce the risk of shortcuts that lead to fragile systems.
Improved Adoption: Cross-functional collaboration between engineering, product, and business teams ensures AI systems solve real problems and are easier to adopt.
Crucial Roles And Skills For An AI Engineering Team
AI engineering teams are composed of individuals with different areas of expertise. Each role contributes to the development, deployment, and maintenance of AI systems. Unlike general software teams, AI teams require deeper skills in data handling, statistical modeling, and integration of learning systems.
1. Machine Learning Engineer
Machine learning engineers build and deploy machine learning models into production systems. They are responsible for taking models developed in research settings and making them perform reliably in real-world environments.
Key skills include Python, TensorFlow or PyTorch, and MLOps tools such as MLflow, Kubeflow, or SageMaker. They write code that connects models to data pipelines, APIs, and user-facing applications.
This role connects research teams and software engineering teams. Many machine learning engineers have backgrounds in computer science, applied mathematics, or physics, often with experience in backend systems or DevOps.
2. Data Scientist
Data scientists focus on analyzing data, running experiments, and building prototype models. Their work often begins with a business question and involves identifying patterns, testing hypotheses, and evaluating model performance.
They apply statistics, probability, and machine learning to create insights. Common tools include Python, R, SQL, and data visualization libraries. They often use notebooks and environments like Jupyter for experimentation.
The role differs from machine learning engineers in that it centers on exploration, not deployment. Many data scientists have backgrounds in statistics, economics, or applied sciences, often with training in research methods.
3. Data Engineer
Data engineers design and maintain the systems that move and store data. They create pipelines that collect, clean, and transform data so it can be used by models.
Skills for this role include SQL, data warehousing technologies, ETL (extract, transform, load) processes, and tools like Apache Spark or Airflow. They focus on data quality, reliability, and scalability.
This role supports the rest of the team by ensuring that AI systems have the data they need. Typical backgrounds include computer science, information systems, or software engineering, often with experience in database administration or large-scale systems.
4. AI Product Manager
AI product managers connect business goals to technical execution. They define what the AI system is supposed to do and make trade-offs between features, performance, and timelines.
They communicate with stakeholders, prioritize tasks, and translate requirements into technical plans. Skills include project management, systems thinking, and an understanding of both machine learning and business strategy.
This role often requires a mix of technical and business education. Many AI product managers have experience in software product management, business analytics, or engineering.
5. Prompt Engineer
Prompt engineers focus on designing effective prompts for large language models (LLMs). These prompts guide LLMs to produce accurate, relevant, and useful outputs.
This role became more important with the rise of generative AI systems such as ChatGPT and Claude. Prompt engineers test phrasing, adjust context, and use feedback loops to improve model behavior.
Key skills include language understanding, iterative testing, and model behavior analysis. Backgrounds vary but often include linguistics, UX research, or experience with LLM fine-tuning or evaluation.
6. Domain Expert
Domain experts provide industry-specific knowledge that helps AI systems make relevant decisions. They help teams understand how the model’s outputs relate to real-world processes or regulations.
They work closely with engineers and scientists to validate model results and interpret complex input data. Their expertise ensures that AI systems are useful and accurate in specific business contexts.
Examples include medical professionals in healthcare AI, financial analysts in fintech, and chemists in pharmaceutical AI. Their backgrounds come from the field they represent, not from software or engineering.
7. AI Ethicist
AI ethicists focus on ensuring that AI systems are developed and used responsibly. They analyze the social impact of AI systems and help prevent issues such as bias, discrimination, and misuse.
This role is gaining importance due to new laws, such as the EU AI Act, and guidelines for responsible AI use. AI ethicists work with legal, compliance, and engineering teams to align systems with ethical and regulatory standards.
Skills include policy analysis, risk assessment, and familiarity with AI fairness and transparency tools. Many AI ethicists come from backgrounds in philosophy, law, public policy, or responsible tech research.
Proven Team Structures For AI Success
AI engineering teams are organized in different ways depending on how a company uses AI, how large the company is, and how experienced it is with machine learning systems. Each structure has its own strengths and trade-offs.
A centralized structure places all AI experts in one team. This design works well in early stages of AI adoption because it helps teams share knowledge and follow the same development practices. Over time, this structure may create gaps in communication between the AI team and specific business units.
An embedded structure places AI engineers directly within business units. These teams work closely with product managers and domain experts. This setup allows AI solutions to be highly relevant to each business area. However, similar problems may be solved multiple times in different parts of the company, leading to duplicated work.
A hub-and-spoke structure combines a central AI team (hub) with smaller, embedded teams (spokes) in individual business units. The hub maintains standards and shared tools, while the spokes adapt AI systems to specific needs. This model can be harder to coordinate but allows for both consistency and flexibility.
A center of excellence is used when an organization wants to manage AI across many teams with a strong focus on governance, compliance, and reuse of tools. This structure helps standardize how AI is used across the company. It can face challenges if it becomes disconnected from day-to-day business operations.
Team structure selection depends on several factors:
Organization size: Larger organizations often benefit from hybrid or hub-and-spoke models. Smaller organizations may prefer centralized teams.
AI maturity level: Teams new to AI often start with a centralized model. More mature teams move toward embedded or hybrid structures.
Business goals: If the goal is to scale AI across many departments, a center of excellence or hub-and-spoke approach may be more appropriate.
Available talent: Some structures require more AI specialists. Others rely more on cross-functional collaboration.
Examples include global banks using centers of excellence to manage AI risk, retail companies using embedded teams to personalize customer experiences, and mid-sized tech firms adopting hub-and-spoke models to support multiple product lines. Each approach depends on the company’s structure, goals, and internal expertise.
In-House Vs External Vs Hybrid AI Engineering Teams
There are three main ways to build an AI engineering team: hiring in-house, working with external partners, or using a hybrid of both. Each approach has different advantages and challenges, depending on the company’s goals, resources, and stage of AI adoption.
1. In-House AI Teams
In-house AI teams are built and managed entirely within the organization. Team members are full-time employees who work closely with other departments.
Advantages:
Full ownership of intellectual property (IP)
Ability to build long-term internal knowledge and culture
Closer alignment with company-specific goals and processes
Challenges:
Difficult to recruit experienced AI talent
Higher upfront and ongoing costs for salaries, tools, and infrastructure
Longer time to reach full team productivity
Best for:
Long-term AI strategy: When AI is core to business strategy
Competitive advantage: When AI capabilities provide market differentiation
Sensitive data: When data security is paramount
2. External Partnerships
External partnerships involve working with outside firms that specialize in AI. These may include consultancies, independent contractors, or boutique AI firms.
Benefits:
Faster access to experienced teams
Specialized expertise in areas like computer vision, NLP, or MLOps
Reduced time to first deployment
Limitations:
Risk of limited internal knowledge transfer
Ongoing external costs can accumulate over time
Less control over day-to-day implementation decisions
Ideal scenarios:
Short-term projects with clear deliverables
Gaps in internal expertise or bandwidth
Pilot programs to test AI feasibility before scaling
3. Hybrid Approach
A hybrid approach combines internal teams with external partners. The internal team sets strategy and manages long-term knowledge, while external experts support specific tasks, tools, or phases of development.
Framework for deciding what to keep in-house vs. outsource:
Keep strategic roles in-house: AI leadership, product direction, and data governance
Outsource specialized or short-term tasks: model prototyping, infrastructure setup, or compliance audits
Co-develop in sensitive areas: domain-specific models or regulated industries
Example:
A healthcare startup built an internal team of machine learning engineers and data scientists to maintain its diagnostic models. It partnered with an external firm to set up its data infrastructure and deploy its models using cloud-based MLOps tools. This allowed the internal team to focus on model accuracy and compliance, while the external experts handled scalability and deployment.
Overcoming Common AI Team Building Challenges
Building AI engineering teams involves several practical challenges. These challenges can affect how quickly a team becomes productive, how well it works with the rest of the organization, and whether it can deliver useful results.
Talent Scarcity
There are fewer experienced AI engineers than open positions. This makes it difficult to hire candidates with the right mix of technical skills and applied experience.
Solutions:
Focus on targeted hiring by narrowing role definitions and aligning them with business needs.
Consider candidates with strong adjacent experience (e.g., backend software engineers or data scientists) and support their transition into AI roles.
Build relationships with academic institutions through internships or research partnerships.
Warning signs:
Open roles stay unfilled for over 3–4 months.
Teams rely heavily on contractors without internal capability.
Job descriptions list too many unrelated technologies or unclear role scopes.
Knowledge Gaps
Existing staff may not have experience with machine learning, data pipelines, or model evaluation. This can slow down collaboration or lead to misunderstandings about how AI systems work.
Solutions:
Offer structured learning paths focused on tools already in use (e.g., PyTorch, SQL, MLflow).
Pair less experienced team members with senior engineers for live projects.
Use internal demos and tech reviews to share context and encourage learning.
Warning signs:
Teams struggle to align on AI project goals.
Engineers avoid AI-related tasks due to lack of confidence.
Business stakeholders expect models to work like traditional software.
Integration Issues
AI systems often operate separately from legacy IT systems. When teams do not coordinate, it becomes difficult to deploy models, monitor their performance, or use existing data pipelines.
Solutions:
Involve IT and DevOps teams early in AI planning.
Align on shared tools and infrastructure standards.
Use APIs and standardized data formats to connect systems.
Warning signs:
Models work in testing but fail in production due to infrastructure gaps.
Data used by AI models is not updated regularly or is incomplete.
Model monitoring is missing from existing observability systems.
Unrealistic Expectations
Stakeholders may expect AI systems to deliver immediate or perfect results. This can cause pressure to deploy incomplete models or pursue goals that are not technically feasible.
Solutions:
Define clear success metrics that reflect incremental progress.
Communicate model limitations and assumptions in plain language.
Run pilot projects before full deployments to gather reliable evidence.
Warning signs:
Timelines are compressed without adjusting scope.
Success is defined only by high-level business goals, not model performance.
Teams are asked to make decisions based on low-confidence predictions.
Example:
A logistics company deployed a route optimization model without involving the operations team. The model worked well in simulation but failed to account for delivery constraints known only to drivers. After a pilot phase revealed these gaps, the team added domain experts to the project. Model performance improved once real-world constraints were included in the data pipeline.
Budget Constraints
Some AI projects require new infrastructure, tools, or time from multiple teams. When budgets are limited, it becomes harder to justify long-term investments or scale promising prototypes.
Solutions:
Prioritize use of open-source tools and cloud credits during early development.
Focus on projects where small models or simple heuristics can deliver measurable gains.
Reuse internal components across multiple AI use cases to reduce duplication.
Warning signs:
Projects are paused due to lack of infrastructure support.
Teams rely on free-tier tools not designed for production workloads.
AI initiatives are evaluated only by short-term financial metrics.
Practical Steps To Align AI With Business Goals
AI projects are more effective when they begin with a clear understanding of the business problem they are intended to solve. Starting with business goals helps teams choose the right data, model type, and success criteria. It also makes it easier to evaluate whether the AI system is useful after it is deployed.
1. Define Clear Success Metrics
Success metrics are used to measure whether an AI project is working as expected. Meaningful metrics are specific, measurable, and linked to business outcomes. Good metrics reflect how the AI system improves a process, solves a problem, or reduces a cost.
Examples of good metrics:
Reduction in customer service response time
Increase in product recommendation click-through rate
Accuracy of a fraud detection model on real transactions
Examples of vanity metrics:
Number of models trained
Total training time
Amount of data processed
Questions to ask when creating AI project metrics:
Does this metric directly connect to business value?
Is this metric measurable in our current systems?
Will this metric drive the right behaviors?
2. Collaborate With Product And Domain Experts
Product managers and domain experts understand the context in which an AI system will be used. Their input helps define the problem, shape requirements, and interpret results. Collaboration between AI engineers and these experts increases the likelihood that the system will be useful and accepted.
Common collaboration methods:
Workshops that include data scientists, product managers, and users
Embedding domain experts into AI teams for the project duration
Regular review meetings to check alignment
Communication barriers can arise when technical and non-technical team members use different vocabulary or assumptions. These can be addressed by defining terms clearly, using visual aids like diagrams, and giving time for questions in meetings.
A simple framework for collaboration meetings:
State the goal of the project
Review data sources and assumptions
Share current results or prototypes
Ask for feedback on whether outputs match expectations
Record action items and next steps
3. Prototype And Validate Early
Early prototyping helps teams test ideas before building full systems. Prototypes are usually small-scale versions of the final model that are fast to build and easy to change. Testing these early with users or stakeholders helps identify problems before they become costly.
A lean approach to AI development includes:
Choosing a narrow use case
Using a limited dataset
Creating a simple model
Getting feedback on results
Signs it may be time to pivot:
Users do not understand or trust model results
Model performance is lower than expected on real-world data
Business needs have changed since the project started
Signs it may be time to continue:
Users find the model helpful, even if imperfect
Feedback suggests clear ways to improve performance
Model results are aligned with business priorities
4. Maintain Iterative Feedback Loops
AI systems often require improvement after deployment. This is because real-world data can change, or users may interact with the system in unexpected ways. Iterative feedback loops allow teams to collect information, make updates, and measure impact over time.
Ways to implement continuous improvement:
Collect user feedback through surveys or support tickets
Monitor model performance on live data
Schedule regular model reviews and retraining
A/B testing allows teams to compare two versions of a model or feature to see which performs better. One group of users sees the original version, while another group sees the new version. Results are compared using predefined metrics.
Template for feedback collection and implementation:
Identify what to measure (e.g., accuracy, user behavior)
Collect data over a set period
Analyze results and compare to previous versions
Decide whether to keep, adjust, or reverse the update
Document changes and inform stakeholders
Driving Sustainable Value Through Leadership And Culture
Leadership and culture influence how AI engineering teams work together, make decisions, and deliver results. Decisions made by leaders affect how teams approach problems, manage uncertainty, and align their work with business goals. Culture shapes daily behaviors, communication patterns, and how people respond to change.
AI projects often involve trial and error. Leaders are responsible for balancing new ideas with realistic constraints. This includes choosing which experiments to pursue, setting clear expectations, and managing timelines and resources.
Teams working on AI systems benefit from psychological safety. This means individuals can share ideas, ask questions, or admit mistakes without fear of negative consequences. When teams feel safe, they are more likely to test new approaches and learn from failures.
Data-driven decision making is a key principle in AI development. Leaders encourage this by supporting access to accurate data, using metrics to evaluate progress, and ensuring that decisions are based on evidence rather than assumptions.
Cross-functional collaboration brings together people with different types of knowledge, such as engineering, product, and domain expertise. Leaders can support this by creating teams with diverse roles, setting shared goals, and encouraging open communication between departments.
Warning signs of cultural issues include:
Resistance to AI adoption: Employees express distrust in AI systems, avoid using them, or question their value without reviewing results. This can occur when teams are not included in the development process or when the system changes existing workflows without explanation.
Data silos: Teams store data in separate systems that are not connected. This limits model performance, reduces visibility, and creates duplication. Silos often form when departments operate independently without shared tools or standards.
Risk aversion: Teams avoid trying new approaches due to fear of failure or negative feedback. This can slow progress and lead to overly cautious development. Risk aversion may result from unclear goals, limited support from leadership, or previous negative experiences.
Technical-business divide: Communication gaps exist between engineering and business teams. This can lead to unclear requirements, misunderstood priorities, or delayed decisions. The divide is often visible when meetings use unclear terminology or when project goals are not shared across functions.
Moving Forward With High-Impact AI Engineering
AI engineering teams function differently from traditional software teams. They rely on specialized roles, structured collaboration, and iterative development to build and maintain systems that learn from data. Organizational design, leadership, and alignment with business goals influence the effectiveness of these teams.
Several models exist for structuring AI teams, including centralized, embedded, hub-and-spoke, and centers of excellence Each. model has trade-offs related to coordination, knowledge sharing, and alignment with domain needs. AI teams may be built in-house, through external partnerships, or as a hybrid. Challenges such as talent shortages, integration gaps, and unrealistic expectations often arise during team development.
Below is a roadmap for starting or expanding an AI engineering team:
First 30 days
Define the purpose of the AI initiative and identify measurable outcomes.
Assess existing infrastructure, data availability, and team capabilities.
Determine whether the team will be built internally, externally, or through a hybrid approach.
First 90 days
Hire or designate critical roles, such as an AI team lead, data engineer, or ML engineer.
Establish metrics for model performance and business alignment.
Begin small-scale prototyping with a focus on a narrow, well-defined use case.
First 6 months
Evaluate early model performance and iterate based on feedback.
Formalize workflows for data access, model deployment, and performance monitoring.
Expand collaboration between AI engineers, product teams, and domain experts.
Start exploring how sedulo search can help you build your AI engineering team by connecting with our specialized recruiters at https://www.sedulo.io/#contact
FAQs About Building An Effective AI Engineering Team
How do I quickly upskill my existing software engineers for AI work?
Prioritize practical learning through paired programming with experienced AI engineers and focused training on machine learning fundamentals, while assigning progressively complex AI tasks that build on their existing software engineering strengths.
What tools should mid-sized companies prioritize when building their first AI team?
Focus on established, enterprise-ready tools like Hugging Face for model access, MLflow for experiment tracking, and cloud-based AI platforms (AWS SageMaker, Azure ML, or Google Vertex AI) that provide scalable infrastructure without requiring extensive DevOps expertise.
How can I measure the ROI of my AI engineering team?
Track both direct metrics (cost savings, revenue generation, process efficiency improvements) and indirect indicators (increased customer satisfaction, employee productivity, and competitive differentiation) while establishing clear baseline measurements before AI implementation.
What are the most critical first hires for a new AI engineering team?
Start with a technically-strong AI team lead who understands both the business context and machine learning fundamentals, paired with either a data engineer (if data infrastructure needs work) or an experienced machine learning engineer (if your data is already well-organized).
How do AI engineering teams differ from traditional software engineering teams?
AI engineering teams require specialized skills in statistics, data science, and machine learning alongside traditional software engineering capabilities, with greater emphasis on experimentation, data quality management, and model performance monitoring throughout the development lifecycle.