AI Team Structures Explained: Who Does What in a Modern AI Department
Artificial Intelligence (AI) and Machine Learning (ML) are no longer confined to research labs and tech giants. In the UK, organisations from healthcare and finance to retail and logistics are adopting AI to solve problems, automate processes, and create new products. With this growth comes the need for well-structured teams.
But what does an AI department actually look like? Who does what? And how do all the moving parts come together to deliver business value?
In this guide, we’ll explain modern AI team structures, break down the responsibilities of each role, explore how teams differ in startups versus enterprises, and highlight what UK employers are looking for. Whether you’re an applicant or an employer, this article will help you understand the anatomy of a successful AI department.
Why AI Team Structure Matters
It’s tempting to think of AI as a purely technical discipline, but team structure is just as important as algorithms.
Scalability – AI projects often start as small pilots. To expand into reliable, enterprise-grade systems, companies need defined hand-offs and specialist roles.
Reliability – A model that works in testing may fail in production if no one is accountable for monitoring it. MLOps and SREs prevent downtime.
Accountability – With multiple people touching the same data and code, clear responsibilities reduce mistakes and finger-pointing.
Attracting talent – Top professionals want clarity on their role and career progression.
Compliance and ethics – With the UK preparing to regulate AI use more heavily, accountability for fairness, privacy, and transparency must be embedded in the team.
Core Roles in a Modern AI Department
Below, we break down the key roles you’ll find in a structured AI department, what each one does, and the skills employers look for in the UK.
Data Analyst
Data analysts sit at the entry point of most AI projects. Their job is to make sense of raw data and turn it into insights the business can use. They clean data, create dashboards, track metrics, and support decision-making.
Typical daily tasks include:
Writing SQL queries to pull data from databases.
Building reports and visualisations in tools like Power BI or Tableau.
Spotting anomalies or trends in datasets.
Working with stakeholders to answer business questions.
Skills include SQL, Excel, BI tools, and basic Python or R scripting. In the UK, data analysts often come from backgrounds in statistics, economics, or business.
Career path: Many move into data engineering or data science roles as they gain programming and modelling skills.Typical UK salary: £30,000 to £45,000 depending on experience.
Data Engineer
Without data engineers, AI projects grind to a halt. These specialists design and maintain pipelines that move data from source systems into data warehouses and lakes. They make sure data is secure, accurate, and accessible.
Daily tasks include:
Building ETL pipelines using tools such as Apache Airflow.
Managing data storage systems like Snowflake, Redshift, or Azure Synapse.
Handling streaming data from IoT or real-time systems.
Ensuring data quality and governance compliance.
Skills include Python or Scala, distributed systems such as Spark, cloud platforms like AWS, GCP, or Azure, and database management.
Career path: Senior engineers may progress to data architecture or ML platform design roles.Typical UK salary: £45,000 to £75,000, with higher rates in London and fintech.
Machine Learning Engineer
Machine learning engineers productionise models, taking prototypes from notebooks into scalable systems. They combine ML knowledge with strong software engineering.
Daily tasks include:
Designing and training models in TensorFlow, PyTorch, or scikit-learn.
Packaging models into APIs or microservices.
Deploying to cloud platforms using Docker and Kubernetes.
Monitoring model performance and retraining when accuracy declines.
ML engineers differ from researchers in that they focus less on theoretical innovation and more on making AI work in the real world.
Career path: Experienced ML engineers often move into AI architect or technical lead roles.Typical UK salary: £55,000 to £90,000, with senior engineers earning six figures in high-demand sectors.
AI Engineer
The AI engineer role is broader and sometimes overlaps with ML engineering. In many companies, the AI engineer is the “jack of all trades” who handles data preparation, modelling, and deployment. They may also specialise in applied AI such as natural language processing or computer vision.
Daily tasks include:
Gathering requirements from business teams.
Building and fine-tuning ML models.
Integrating AI into existing applications.
Creating APIs and interfaces for AI-powered features.
This role requires both technical skills and product awareness. In the UK, many AI engineer job descriptions overlap with data scientist or ML engineer roles, so clarity is key.
Career path: AI engineers can move into product ownership or specialise further into areas like NLP engineering.Typical UK salary: £50,000 to £85,000 depending on sector.
MLOps Engineer
MLOps engineers ensure models don’t just launch, but stay reliable over time. They apply DevOps principles to machine learning.
Daily tasks include:
Automating training and deployment pipelines.
Tracking experiments and managing model versioning.
Monitoring for data drift or performance decline.
Scaling infrastructure to handle production loads.
Key skills include Docker, Kubernetes, MLflow, cloud services, and monitoring tools like Prometheus or Grafana.
Career path: Senior MLOps engineers may move into ML platform architecture or engineering leadership.Typical UK salary: £60,000 to £95,000.
AI Researcher
AI researchers, or research scientists, focus on advancing knowledge. They design new algorithms and architectures rather than productionising models.
Daily tasks include:
Reviewing the latest research papers.
Designing and running experiments.
Writing code to test new approaches.
Publishing results and contributing to open-source projects.
This role typically requires a PhD or strong academic background. Researchers are more common in large enterprises or R&D labs than in small businesses.
Career path: Senior researchers may lead research teams or transition into applied AI leadership.Typical UK salary: £55,000 to £100,000, with higher ranges in big tech or finance.
DevOps and Site Reliability Engineer (SRE)
While MLOps engineers focus on ML systems, DevOps professionals and SREs ensure overall infrastructure reliability.
Daily tasks include:
Maintaining uptime and system monitoring.
Automating deployments and updates.
Managing cloud infrastructure, GPUs, and networks.
Ensuring security and compliance with standards.
This role is vital in large-scale AI projects where downtime or breaches could be costly.
Typical UK salary: £50,000 to £85,000.
Product Manager
Product managers act as translators between business needs and technical delivery. They define the purpose of AI projects and ensure they deliver measurable value.
Daily tasks include:
Working with stakeholders to identify opportunities.
Setting KPIs and success metrics.
Prioritising features and managing timelines.
Coordinating technical and non-technical teams.
Strong communication and business skills are critical. Product managers may not code but must understand AI well enough to guide strategy.
Typical UK salary: £55,000 to £95,000.
AI or ML Architect
The architect designs the big picture of how AI systems fit together. They decide on tools, frameworks, and infrastructure strategies.
Daily tasks include:
Designing scalable architectures.
Evaluating cloud vs on-premise options.
Setting standards for teams to follow.
Guiding long-term technical strategy.
This is a senior role requiring years of experience across data and ML engineering.
Typical UK salary: £80,000 to £120,000.
Responsible AI Specialist
Responsible AI specialists ensure fairness, transparency, and compliance. With growing regulation, this role is becoming critical in UK organisations.
Daily tasks include:
Auditing datasets for bias.
Ensuring GDPR and privacy compliance.
Defining explainability and interpretability standards.
Training teams on responsible practices.
This role requires knowledge of both AI and regulation, as well as strong communication skills.
Typical UK salary: £45,000 to £80,000.
Collaboration Across the AI Lifecycle
An AI project usually follows these stages:
Ideation – Product managers, architects, and ethics specialists define the goals and constraints.
Data preparation – Data engineers and analysts collect and clean the data.
Modelling – ML engineers and researchers build and test models.
Infrastructure setup – Engineers and MLOps specialists create the pipelines and environments.
Deployment – ML engineers, AI engineers, and SREs deploy and monitor systems.
Monitoring and retraining – MLOps ensures reliability, while ethics specialists track fairness.
Scaling – Architects and product managers refine systems for growth.
Startups vs Enterprises
Startups – One person may wear multiple hats. An AI engineer might also manage data pipelines and deployments. Speed matters more than structure.
Growing companies – As use-cases expand, bottlenecks appear. Dedicated roles for data engineering, MLOps, and product management emerge.
Enterprises – Teams become fully specialised. Ethical oversight, governance, and architecture roles are added. In UK finance, healthcare, and government, compliance is a major driver of structure.
Skills and Education in the UK
Employers typically look for:
Data analysts – maths, statistics, or economics degrees; SQL and BI tools.
Data engineers – computer science or engineering backgrounds; cloud and distributed systems knowledge.
ML and AI engineers – strong programming skills, ML frameworks, and cloud deployment.
MLOps – DevOps background with ML pipeline knowledge.
Researchers – PhDs or equivalent research portfolios.
Ethics specialists – diverse backgrounds, often law, data, or philosophy combined with AI literacy.
Challenges in Role Definition
Job titles are often used interchangeably, causing confusion.
Prototypes may not transition smoothly into production.
Ethics is often left until too late.
Communication gaps between technical and non-technical staff create friction.
UK companies face a skills shortage, making recruitment difficult.
FAQs
What is the difference between a Machine Learning Engineer and a Data Scientist?A data scientist focuses on analysing data, building models, and exploring possibilities. A machine learning engineer focuses on turning those models into scalable production systems.
Do all AI teams need an ethics specialist?In small teams, ethical responsibilities may be shared. In larger organisations, especially in regulated industries, a dedicated responsible AI role is strongly advised.
Is an AI engineer the same as an ML engineer?Not always. AI engineer is often a broader role, sometimes covering applied AI such as NLP or computer vision, while ML engineer focuses specifically on machine learning models.
What’s the most in-demand AI role in the UK?Currently, data engineers, ML engineers, and MLOps specialists are the most in-demand, as businesses struggle to scale prototypes into production.
Final Thoughts
AI is not just about algorithms – it is about teams. A well-structured AI department brings together analysts, engineers, researchers, and managers into a cohesive unit. For UK organisations, this means not only building reliable and scalable AI but also ensuring compliance, ethics, and long-term sustainability.
For job seekers, understanding these roles helps target applications more effectively. For employers, clear definitions prevent confusion and attract the right talent.
Modern AI is a team sport. By knowing who does what, businesses can unlock the true value of artificial intelligence.
