AI Engineer Jobs

Developers who build and deploy AI systems, from research to production. A role that bridges the gap between theory and application, with a focus on creating intelligent software solutions.

Open roles
139
Salary range
£25k – £100k
Hiring companies
77

AI Engineers are at the forefront of technological innovation, designing and implementing AI systems that solve complex problems. They work across a range of industries, from tech giants to research-heavy startups, and their role involves both theoretical research and practical application. AI Engineers are responsible for developing algorithms, models, and systems that can learn from data, make predictions, and automate tasks. This role requires a strong foundation in computer science, mathematics, and statistics, as well as proficiency in programming languages like Python and frameworks like TensorFlow and PyTorch.

What the role does

Inside the role of an AI Engineer

A typical week for an AI Engineer is a mix of research, development, and collaboration. They spend time coding, testing, and refining models, while also engaging with cross-functional teams to integrate AI solutions into existing systems.

  1. 01
    Design and implement machine learning models
  2. 02
    Optimise algorithms for performance and efficiency
  3. 03
    Collaborate with data scientists and software engineers
  4. 04
    Conduct experiments and analyse results
  5. 05
    Document processes and findings
  6. 06
    Stay updated with the latest AI research and tools
Salary on the board

£25k – £100k

Based on advertised midpoints across the 591 UK listings priced in pounds in the last 12 months. Base salary only.

Salary visibility
44% of listings advertise a salary — up from 0% the year before.
By seniority
£k base
Entry
25
45
127 jobs
Junior
26
63
8 jobs
Mid
44
100
76 jobs
Senior
60
115
88 jobs
Lead
60
120
30 jobs
Skills & tools

What hiring managers ask for

% of 295 listings posted in the last 12 months that mention each skill, extracted from job descriptions.

Python
73%
Machine Learning
35%
RAG
31%
LLM
26%
Prompt Engineering
22%
CI/CD
22%
Generative AI
21%
AI
20%
AWS
17%
LLMs
16%
Azure
16%
SQL
15%
Career ladder

From Junior to Principal

A typical UK progression for ai engineers. Years are guidance — strong people move faster, and many senior folks sidestep into research, product or management.

  1. Level 1

    Junior AI Engineer

    0–2 yrs

    Assist in the development and testing of AI models, with a focus on learning and gaining hands-on experience.

  2. Level 2

    AI Engineer

    2–5 yrs

    Take ownership of specific projects, from initial design to deployment, and contribute to the overall architecture of AI systems.

  3. Level 3

    Senior AI Engineer

    5–8 yrs

    Lead the development of complex AI solutions, mentor junior team members, and ensure the quality and reliability of AI systems.

  4. Level 4

    Principal AI Engineer

    8+ yrs

    Drive strategic initiatives, innovate in AI research, and guide the technical direction of the organisation.

Pathway

How to become a AI Engineer

There's no single route, but most people follow some version of these steps.

  1. 1

    Learn the Fundamentals

    Gain a strong foundation in computer science, mathematics, and statistics. Familiarise yourself with programming languages like Python and AI frameworks.

  2. 2

    Build Practical Skills

    Work on personal or open-source projects to apply your knowledge. Participate in hackathons and coding challenges to gain real-world experience.

  3. 3

    Gain Industry Experience

    Start your career as a Junior AI Engineer, working on smaller projects and learning from more experienced colleagues.

  4. 4

    Specialise in a Domain

    Choose a specific area of AI, such as computer vision or natural language processing, and deepen your expertise in that domain.

  5. 5

    Lead Projects

    Take on more responsibility by leading AI projects, managing teams, and ensuring the successful delivery of AI solutions.

  6. 6

    Innovate and Influence

    Contribute to cutting-edge research, influence the direction of AI in your organisation, and mentor the next generation of AI Engineers.

Live jobs

139 live roles

See all 139 roles →

Experis · 501–1000 people · Late stage

AI Engineer

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  • London
  • Hybrid
  • £600 – £644 pd
  • Mid-level
  • Contract

Builds production-grade GenAI and Agentic AI products within a product squad, using LLMs, RAG and agentic workflows. Requires strong Python engineering plus LangGraph, vector search, evaluation and Azure AI services to deliver secure, maintainable AI applications.

Azure · Azure OpenAI · Azure AI Search +7

SC clearance 3 weeks ago

Luminance · 51–200 people · Series C

AI Engineer

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  • Cambridge
  • Hybrid
  • Permanent

Builds AI prototypes combining multiple components to solve complex, nuanced tasks, integrating novel agentic technologies such as memory usage and personalization. Productionises solutions into reusable libraries and frameworks, shipping features and tools for large enterprise customers from day one. Works with LLMs like GPT, Claude and Mistral in a fast-moving, quality-focused team.

GPT · Claude · Mistral

17 months ago

NXA Talent LTD

AI Engineer

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  • London
  • Hybrid
  • £500 pd
  • Mid-level
  • Contract

Design and build agentic AI systems with multi-step reasoning, multi-agent orchestration and scalable AI microservices in Python and Azure OpenAI. Develop RAG architectures, vector search and orchestration workflows using Semantic Kernel and LangGraph, integrating enterprise systems via MCP. The role also covers AI governance, safety and deploying LLM services into production within a large retail environment.

Azure OpenAI · Azure AI Foundry · Azure AI Search +7

Last week

Ageas Insurance Limited

AI Engineer

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  • London
  • Hybrid
  • £70,000 pa
  • Mid-level

Joins a Data Science & AI team to identify high-value GenAI use cases and productionise scalable AI services on AWS. Works in a cross-functional squad with DevOps, data engineers and Infosec, enhancing MLOps, CI/CD pipelines and an in-house AI Python library while assessing third-party LLM technologies.

AWS · pandas · scikit-learn +6

Last week

Hyre Ai

AI Engineer

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  • London
  • On-site
  • £50,000 – £60,000 pa
  • Mid-level
  • Permanent

Design and evolve production multi-agent systems using Anthropic, OpenAI and Perplexity models on AWS, extending the company's MCP server with new tools. Build the eval and observability layer with golden datasets, regression suites and LLM-as-judge, own AWS infrastructure (ECS Fargate, Lambda, Step Functions, Bedrock) and guard against prompt injection and PII leakage. Set technical direction and mentor engineers.

Anthropic · OpenAI · Perplexity +7

2 weeks ago

Huxley

AI Engineer

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  • London
  • £90,000 – £120,000 pa

AI EngineerLondon (Hybrid, 1 Day per Week Onsite)Are you ready to help build the future of AI from the ground up?Our client isn't buying off-the-shelf solutions or stitching together existing products. They're building a proprietary AI platform from first principles,…

Last month

KO2 Embedded Recruitment Solutions

AI Engineer

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  • Butterrow
  • £55,000 – £65,000 pa

Designs and builds real-time embedded software in C and C++ for sensors and microelectronics, alongside ML pipelines, CI/CD, and live model monitoring. Develops generative AI and LLM systems including RAG and fine-tuning, plus Spark or dbt ETL pipelines and IoT data services. Works in a small team owning the full SDLC from clean-sheet design to tested, deployed code.

Spark · dbt · CI/CD +1

SC clearance Last week

Harnham - Data and Analytics Recruitment

AI Engineer

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  • London
  • Hybrid · 3 days in office
  • £80,000 – £85,000 pa
  • Mid-level

Design, build and deploy scalable AI and machine learning solutions from concept to production, including Generative AI applications using LLMs and orchestration frameworks. Write production-grade Python, apply MLOps practices such as CI/CD, containerisation and monitoring, and build data pipelines for training, fine-tuning and inference. Work hybrid in London alongside data scientists, engineers and business stakeholders on large-scale datasets.

LLMs · Orchestration Frameworks

3 months ago
FAQs

Common questions

  • Python is the most widely used language in AI, but knowledge of C++, Java, and R can also be beneficial.

  • Strong programming skills, a solid understanding of algorithms and data structures, and proficiency in machine learning frameworks are crucial.

  • While a PhD can be beneficial, especially for research roles, many successful AI Engineers have backgrounds in computer science or related fields without a PhD.

  • Salaries for AI Engineers can vary widely based on experience, location, and industry. For more detailed salary information, please refer to the salary section on this page.

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