Cybotrix Technologies is inviting applications for AI / ML Engineers Jobs in UK across entry, mid and senior-level opportunities. We are looking for engineers who can design, train, evaluate, deploy and improve machine learning systems that solve practical business and product problems.
Opportunities may involve traditional machine learning, deep learning, NLP, computer vision, Generative AI, Large Language Models (LLMs), recommendation systems and production ML platforms. Candidates should combine strong programming fundamentals with an understanding of data, algorithms, model evaluation and real-world deployment.
Job Information
Company: Cybotrix Technologies
Position: AI / ML Engineer
Location: United Kingdom – Multiple Locations
Key Locations: London, Manchester, Birmingham, Bristol, Cambridge, Edinburgh, Glasgow, Leeds, Oxford & Reading
Experience: Entry to Senior Level
Employment Type: Full-Time / Role Dependent
Job Category: Artificial Intelligence / Machine Learning
Salary: Open for the right candidates
Qualification: Degree in Computer Science, AI, Machine Learning, Data Science, Mathematics, Engineering or equivalent practical experience
Core Skills: Python, Machine Learning, Deep Learning, SQL, Scikit-learn, TensorFlow/PyTorch, Model Deployment & MLOps
About the Role
AI / ML Engineers working with opportunities supported by Cybotrix Technologies may contribute to the complete machine learning lifecycle.
Rather than working only on experimental models, engineers may be expected to transform business requirements and data into reliable AI solutions that can operate within real applications.
Depending on the project, your work could include:
Data Preparation → Feature Engineering → Model Development → Evaluation → API Integration → Deployment → Monitoring → Continuous Improvement
The exact responsibilities will depend on experience level, project requirements and the employer’s technology environment.
Key Responsibilities
- Design and develop machine learning solutions.
- Analyse structured and unstructured datasets.
- Prepare data for model training.
- Build reusable feature-engineering pipelines.
- Select appropriate ML algorithms.
- Train, validate and evaluate models.
- Perform hyperparameter tuning.
- Compare model performance using suitable metrics.
- Develop inference pipelines.
- Expose ML capabilities through APIs or services.
- Deploy models into production environments.
- Monitor model performance after deployment.
- Investigate model degradation and data drift.
- Collaborate with software, data and platform engineers.
- Maintain experiments and model versions.
- Document model behaviour and technical decisions.
- Improve the reliability and scalability of ML systems.
Python for AI & Machine Learning
Strong Python knowledge is important for most AI/ML engineering positions.
Candidates should be comfortable with technologies such as:
Python, NumPy, Pandas, Scikit-learn and SQL.
Depending on the role, experience with TensorFlow, PyTorch or Keras may also be required.
Engineers should understand the code they write rather than depending entirely on high-level libraries or generated solutions.
Machine Learning
Candidates should understand the practical application of common machine learning techniques.
Relevant areas may include:
Linear Regression, Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, XGBoost, Classification, Clustering, Ensemble Methods and Recommendation Techniques.
More importantly, candidates should understand when an algorithm is appropriate and how to evaluate whether the resulting model is actually useful.
Feature Engineering & Data Preparation
Model quality depends heavily on data quality.
AI/ML Engineers may be responsible for:
- Cleaning datasets.
- Handling missing values.
- Detecting unusual data.
- Encoding categorical variables.
- Scaling numerical features.
- Creating useful features.
- Preventing data leakage.
- Preparing training and validation datasets.
Candidates should understand why each transformation is performed rather than simply applying preprocessing functions.
Model Evaluation
Candidates should know how to select appropriate evaluation methods based on the problem.
Relevant metrics can include:
Accuracy, Precision, Recall, F1 Score, ROC-AUC, Confusion Matrix, MAE, MSE and RMSE.
Engineers should also understand concepts such as:
Overfitting, Underfitting, Bias, Variance, Cross-Validation and Class Imbalance.
A model with high accuracy is not automatically a good model; the metric must match the business and technical objective.
Deep Learning
Some AI/ML opportunities may require experience with deep-learning systems.
Relevant knowledge can include:
Neural Networks, CNNs, RNNs, LSTMs, Transformers, Embeddings and Transfer Learning.
Experience with PyTorch or TensorFlow can be valuable for projects involving advanced AI workloads.
Deep-learning expertise is not mandatory for every machine-learning role.
Generative AI & LLM Engineering
Some UK opportunities may involve Generative AI applications.
Relevant experience can include:
Large Language Models, Prompt Engineering, Embeddings, Vector Search, Retrieval-Augmented Generation (RAG), Model APIs and LLM Evaluation.
Engineers may work on applications such as:
- Intelligent search
- Knowledge assistants
- Document processing
- Information extraction
- Enterprise copilots
- Text classification
- Content analysis
- Conversational interfaces
Candidates should understand that building a production Generative AI application involves more than sending prompts to a model.
RAG & Vector Search
For roles involving LLM applications, knowledge of Retrieval-Augmented Generation can be beneficial.
Candidates may work with:
Document Chunking, Embeddings, Vector Databases, Semantic Search, Retrieval, Re-ranking and Context Construction.
Exposure to vector technologies such as FAISS, Pinecone, Weaviate, Milvus, pgvector, or comparable systems can be useful depending on the project.
NLP & Computer Vision
Some positions may specialise in Natural Language Processing or Computer Vision.
NLP exposure can include:
Text Classification, Named Entity Recognition, Sentiment Analysis, Information Extraction, Semantic Search and Transformers.
Computer-vision exposure may include:
Image Classification, Object Detection, Image Processing and Deep Neural Networks.
These are specialised areas and are not required for every AI/ML Engineer position.
Model Deployment
Production deployment is an important differentiator between experimental machine learning and ML engineering.
Candidates may be expected to package models and expose predictions through services using technologies such as:
FastAPI, Flask, Docker and REST APIs.
Engineers should understand how applications consume model predictions and how deployed services are monitored.
MLOps
Experienced AI/ML Engineers may work with MLOps processes covering model experimentation, deployment and monitoring.
Useful technologies can include:
MLflow, Airflow, Docker, Kubernetes, Git and CI/CD pipelines.
Responsibilities may involve:
- Experiment tracking
- Model versioning
- Pipeline automation
- Model deployment
- Monitoring
- Retraining workflows
- Reproducibility
- Model lifecycle management
Cloud AI & ML
Depending on the employer, projects may run on:
AWS, Microsoft Azure or Google Cloud Platform.
Useful exposure can include managed machine-learning platforms, cloud storage, containers, compute services, data platforms and model-serving infrastructure.
Candidates are not expected to have expertise across every cloud provider.
Entry-Level AI / ML Engineer Roles
Entry-level candidates should have strong fundamentals in:
Python, SQL, Statistics, Data Preparation, Machine Learning Algorithms and Model Evaluation.
Academic projects, internships, research projects, GitHub repositories and practical ML applications can strengthen a candidate’s profile.
Candidates should be able to explain:
- The problem they attempted to solve.
- Why they selected a particular algorithm.
- How the data was prepared.
- How the model was evaluated.
- What problems occurred.
- How the solution could be improved.
A project that a candidate genuinely understands is more valuable than a large list of unexplained AI technologies.
Mid-Level AI / ML Engineer Roles
Mid-level engineers should be able to independently develop and improve machine-learning solutions.
Typical expectations can include:
- Building training pipelines.
- Performing feature engineering.
- Selecting algorithms.
- Tuning models.
- Developing inference APIs.
- Deploying models.
- Working with cloud infrastructure.
- Tracking experiments.
- Debugging data and model problems.
- Collaborating with software and data teams.
Candidates should demonstrate experience taking models beyond notebooks and into usable applications.
Senior AI / ML Engineer Roles
Senior engineers may take ownership of complex AI systems and technical architecture.
Responsibilities can include:
- Designing scalable ML architectures.
- Selecting modelling approaches.
- Defining model-development standards.
- Reviewing ML code and experiments.
- Designing production inference systems.
- Improving training and deployment pipelines.
- Establishing monitoring strategies.
- Managing model performance and reliability.
- Evaluating emerging AI technologies.
- Mentoring junior engineers.
- Working with engineering and product leadership.
Senior candidates should be prepared to discuss technical trade-offs rather than only individual algorithms or libraries.
AI / ML Engineering Skills
A strong profile may include a combination of:
Programming: Python
Data: SQL, Pandas, NumPy
Machine Learning: Scikit-learn, XGBoost
Deep Learning: PyTorch / TensorFlow
APIs: FastAPI / Flask
Generative AI: LLMs, RAG, Embeddings
MLOps: MLflow / Airflow
Containers: Docker / Kubernetes
Version Control: Git
Cloud: AWS / Azure / GCP
Databases: SQL / NoSQL / Vector Databases
Candidates are not expected to know every technology listed. Required skills will vary by individual vacancy.
What Cybotrix Technologies Looks For
Cybotrix Technologies looks for candidates who combine AI/ML knowledge with practical engineering ability.
Strong candidates should be able to explain not only what model they built, but also:
- Why that approach was selected.
- How training data was prepared.
- Which evaluation metrics were used.
- How overfitting was controlled.
- How the model was deployed.
- How predictions were monitored.
- What limitations existed.
- How the system could be improved.
For experienced positions, practical production experience can be particularly valuable.
What to Include on Your CV
Candidates should highlight measurable AI/ML project experience wherever possible.
Include details such as:
Models Developed, Datasets Used, Algorithms Selected, Evaluation Metrics, APIs Built, Models Deployed, Cloud Platforms, MLOps Tools and Production Problems Solved.
Entry-level candidates should include relevant university projects, internships, research, competitions, GitHub repositories or independently developed AI applications.
Experienced candidates should focus on their individual contribution rather than describing only the overall team project.
AI / ML Engineer Opportunities Across the UK
Cybotrix Technologies supports hiring requirements for AI and machine-learning talent across multiple UK technology markets.
Opportunities may be available in London, Manchester, Birmingham, Bristol, Cambridge, Edinburgh, Glasgow, Leeds, Oxford, Reading, and other UK locations depending on current employer requirements.
Work arrangements may vary between on-site, hybrid and remote-compatible positions based on the individual vacancy.
Apply for AI / ML Engineer Jobs in UK
Candidates searching for AI / ML Engineers Jobs in UK can apply for relevant entry, mid and senior-level opportunities through Cybotrix Technologies.
Technical interviews may assess Python, SQL, statistics, machine learning algorithms, feature engineering, model evaluation, deep learning, APIs, model deployment, MLOps, cloud platforms, Generative AI and practical problem-solving ability, depending on the position.
Salary is open for the right candidates and will depend on experience level, technical expertise, location, project requirements and individual employer expectations.