AI Engineer: Build Scalable LLM & AI Solutions

Addition; Gloucester; On-site; GBP 70,000 - 110,000; Full time

Job summary

Addition is seeking experienced AI Engineers to design, develop, and deploy AI-powered solutions delivering real-world outcomes across government, defence, national security, and commercial sectors. You will work across full AI lifecycle, implement MLOps, and apply secure, scalable cloud-native architectures using Python and modern ML frameworks.

Collaboration with multidisciplinary teams is essential to translate requirements into production systems.

Qualifications

• Degree in Computer Science or closely related field.
• Experience with AI/ML lifecycle from design to deployment.
• Strong software engineering practices including CI/CD, testing, and code reviews.

Responsibilities

• Design, develop, and deploy AI-enabled applications.
• Build ML/DL/Generative AI solutions.
• Develop RAG pipelines and LLM-powered solutions.
• Integrate AI services with cloud platforms and enterprise systems.
• Create scalable data processing and feature engineering pipelines.
• Implement MLOps including deployment, monitoring, and lifecycle management.
• Evaluate model performance and improve accuracy.
• Ensure secure, scalable, maintainable AI solutions.

Skills

Python

PyTorch

TensorFlow

Scikit-Learn

MLOps

Docker

Kubernetes

Cloud platforms

APIs

Microservices

Distributed systems

NLP

Computer vision

Reinforcement learning

Predictive analytics

Responsible AI

AI governance

DevOps

Platform Engineering

Security

Education

Degree in Computer Science/AI/Data Science/Software Engineering

Tools

MLflow

Kubeflow

SageMaker

Azure ML

GCP

Vector databases

Job description

Addition is seeking experienced AI Engineers to design, develop, and deploy AI-powered solutions delivering real-world outcomes across government, defence, national security, and commercial sectors. You will work across full AI lifecycle, implement MLOps, and apply secure, scalable cloud-native architectures using Python and modern ML frameworks.

Collaboration with multidisciplinary teams is essential to translate requirements into production systems.