AI engineer job description.
Hiring an AI engineer or exploring your next step in artificial intelligence? This AI engineer job description outlines responsibilities such as integrating machine learning models, developing smart systems, and managing model performance. It also covers in-demand skills and expected salary ranges.
What does an AI engineer do?
An AI engineer designs, builds, and deploys artificial intelligence systems that automate tasks, generate insights, or enhance user experiences. They apply machine learning models, optimise performance, and integrate AI into business processes.
Responsibilities include selecting appropriate models, managing training data, evaluating accuracy, and deploying AI-powered solutions via APIs or microservices. Tools commonly include Python, TensorFlow, Keras, or MLFlow, and platforms like AWS SageMaker or Azure ML.
In startups, AI engineers may lead innovation across multiple use cases. In enterprise settings, they often work within product or data teams to implement intelligent features at scale, such as recommendation engines or fraud detection systems.
Key responsibilities of an AI engineer.
AI engineers develop and deploy intelligent systems that automate decisions and behaviours. Their responsibilities include:
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Designing AI systems using deep learning, reinforcement learning, or rule-based logic
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Collaborating with data scientists to transition models from research to production
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Developing APIs and microservices to serve models
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Testing and validating AI performance across use cases
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Implementing pipelines for feature engineering and model retraining
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Supporting infrastructure for experimentation and model monitoring
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Ensuring models are secure, fair, and explainable
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Managing model deployment in cloud environments (e.g. SageMaker, Vertex AI)
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Documenting system behaviour, inputs, and outputs
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Working closely with product teams to embed AI into workflows
This role blends machine learning with software engineering and systems integration.
Skills and requirements for an AI engineer
AI engineers develop intelligent systems solving complex problems. Employers typically look for:
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4–7 years of experience in AI, ML, or data science roles
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Experience building and deploying ML or deep learning models
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Proficiency in Python, ML frameworks, and model evaluation
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Familiarity with MLOps, deployment pipelines, and APIs
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Ability to collaborate with software engineers and data scientists
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Strong communication skills translating AI into business impact
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Skilled in model tuning, monitoring, and improvement
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Understanding of privacy, bias, and AI risk considerations
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Experience applying AI in real-time or large-scale systems
Most AI engineers productionise models in software or customer-facing applications.
Average salary for an AI engineer.
In the UK, the average salary for an AI engineer typically ranges from £50,000 to £80,000, depending on model development, deployment capabilities, and platform scalability.
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Mid-level AI engineers tend to earn between £50,000 and £65,000
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Senior engineers creating scalable AI applications in cloud environments may earn between £66,000 and £80,000
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MLOps, explainability, and model optimisation are key salary boosters
Best pay is found in deep tech startups, SaaS platforms, and applied AI businesses.
Career progression for an AI engineer.
An AI engineer builds and deploys intelligent systems — spanning predictive modelling, automation, and generative applications. It’s a versatile and fast-evolving career path with high demand. A typical journey includes:
Data scientist
Builds ML models, preps training data, and supports model evaluation.
AI engineer
Develops end-to-end pipelines for computer vision, NLP, or tabular data use cases. Integrates models into production.
Senior AI engineer
Leads experimentation, improves model performance, and works closely with product or engineering teams.
Applied AI lead
Owns infrastructure, tooling, and monitoring for large-scale AI systems.
Head of AI
Leads teams, strategy, and innovation across AI functions. Aligns projects with commercial and ethical considerations.
salary guide
Our UK data salary guide.
AI engineers develop and deploy intelligent systems across platforms and products. Offers should reflect experience with ML models, APIs, and infrastructure integration.
Our UK data salary guide includes AI benchmarks, 2024 data comparisons, and projections for senior AI roles through to 2026.
FAQS
AI engineer FAQs.
They apply ML, NLP, or CV techniques to business problems — often blending experimentation with deployment. Projects may include fraud detection, personalisation, recommendation systems, or chatbot optimisation, depending on the sector.
AI engineers are usually closer to production code. They write scalable pipelines, manage model lifecycle in real-time systems, and collaborate with backend teams. Data scientists often explore hypotheses; AI engineers focus on operationalising insights.
That a generic ML background is enough. In reality, AI engineers often need strong software engineering skills, exposure to DevOps practices, and familiarity with model versioning, reproducibility, and latency constraints.
Not just technical skill, but awareness of business context. Look for engineers who can explain how their model’s output impacts KPIs, how they monitor drift, and what trade-offs they made between complexity and stability.
Progression includes machine learning architect, AI platform lead, or domain-specific specialisation (e.g. NLP, CV, or reinforcement learning lead) — especially in product-centric businesses.
