AI Engineer

Team Up - We Build Teams

. Design, develop, and deploy machine learning and AI systems for real-world applications .

Posted 9/15/2026full-timeRemote • United KingdomJuniorMid-LevelWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing, developing, and deploying machine learning and AI systems, with a strong focus on model optimization and MLOps practices. Proficient in Python and familiar with tools like PyTorch, TensorFlow, and cloud platforms for effective model training and serving.

Highest-signal resume keywords

Machine Learning Model DevelopmentPython ProgrammingMLOps Tools FamiliarityData Preprocessing and Feature EngineeringCloud Platform Experience

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningAI Model DevelopmentData MiningFeature EngineeringDeep Learning ArchitecturesSupervised LearningUnsupervised LearningModel Evaluation MetricsOptimization TechniquesContainerized Deployment

Soft Skills

Analytical SkillsProblem-SolvingCommunication SkillsCuriosity-Driven Mindset

Tools & Technologies

PyTorchTensorFlowDockerGitMLflowKubeflowSageMakerAWS Lambda

Industry Keywords

Machine LearningArtificial IntelligenceNatural Language ProcessingComputer VisionMLOps

Tech Stack

Tools & technologies

AWSCloudDockerPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Design, develop, and deploy machine learning and AI systems for real-world applications
Build and optimize custom AI models for domain-specific tasks
Design and maintain data mining, data preprocessing, and data-labeling pipelines
Work with large datasets, including feature engineering and data preparation
Apply machine learning techniques across areas such as LLMs, NLP, computer vision, or other AI domains
Train, evaluate, optimize, and improve machine learning models
Develop and maintain model deployment and MLOps pipelines
Deploy and serve models using cloud platforms and containerized environments
Use tools such as Docker, Git, and relevant MLOps platforms in collaborative development workflows
Analyze model performance and identify opportunities for improvement
Communicate technical concepts and project progress to non-technical stakeholders

Requirements

What you’ll need

2–5 years of hands-on experience building and deploying ML/AI models in production environments
Strong proficiency in Python
Practical experience with PyTorch and/or TensorFlow
Experience designing and architecting custom AI models
Experience with LLMs, NLP, computer vision, or other AI domains
Strong understanding of supervised and unsupervised learning, deep learning architectures, optimization, and evaluation metrics
Experience with data preprocessing, feature engineering, data mining, and data-labeling pipelines
Familiarity with MLOps tools and model deployment platforms such as MLflow, Kubeflow, or SageMaker
Knowledge of cloud platforms and services used for model training and serving, such as AWS Lambda
Experience with Docker and containerized model deployment
Familiarity with Git and collaborative software development practices
Strong analytical and problem-solving skills
Ability to communicate complex technical concepts to non-technical stakeholders
Curiosity-driven mindset and comfort working with ambiguity

Benefits

Comp & perks

Remote work arrangement