AI Engineer

Role overview

Responsibilities

• check_circle Design, develop, and deploy production-ready AI and machine learning models.
• check_circle Build and maintain scalable MLOps pipelines for model training, deployment, monitoring, and lifecycle management.
• check_circle Develop and implement Large Language Model (LLM) solutions, Retrieval-Augmented Generation (RAG) pipelines, and Generative AI applications.
• check_circle Integrate AI and ML capabilities into enterprise applications, APIs, and existing platforms.
• check_circle Collaborate with data scientists, software engineers, architects, and product teams to deliver innovative AI solutions.
• check_circle Optimise model performance, scalability, reliability, and operational efficiency.
• check_circle Promote responsible AI practices, including governance, explainability, fairness, and security.

Basic qualifications

• 5–8 years of hands-on experience in AI/ML Engineering (7–8 years preferred).
• Strong programming skills in Python.
• Experience with machine learning frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Proven experience working with Large Language Models (LLMs), prompt engineering, and RAG architectures.
• Hands-on experience with cloud AI platforms such as AWS SageMaker, Azure Machine Learning, or Google Vertex AI.
• Experience with MLOps tools such as MLflow, Kubeflow, or similar platforms.
• Knowledge of vector databases including Pinecone, Weaviate, or FAISS.
• Strong understanding of data engineering, REST APIs, microservices, and cloud-native architectures.
• Excellent problem-solving and communication skills.
• Eligible to work in the UK.

Preferred qualifications

• Experience with Docker and Kubernetes.
• Knowledge of CI/CD pipelines for AI/ML deployments.
• Experience with enterprise-scale AI implementations.
• Exposure to Agile delivery environments.