Role overview
We have a great opportunity for an experienced AI Engineer to build, deploy, and maintain our innovative AI-driven products and solutions. This role requires strong, hands-on technical skills in artificial intelligence and machine learning to translate architectural designs into robust, scalable, and ethical AI systems. Working closely with architects and data scientists, you will be a key builder of the AI ecosystem that addresses our core business challenges.
This role will be based from our Newcastle head office with a hybrid working arrangement.
What you'll work on
- Develop, test, and maintain scalable, high-performance ML models that align with architectural designs.
- Implement and deploy AI/ML solutions by collaborating with stakeholders to understand technical requirements.
- Contribute to the development of the end-to-end AI solution architecture.
- Follow and contribute to MLOps best practices to streamline the development lifecycle.
- Create clear technical documentation for ML models, code, and operational procedures.
- Implement and test solutions to ensure AI performance, fairness, and adherence to ethical guidelines.
- Implement and maintain robust security measures within our AI systems and data pipelines.
- Collaborate effectively with team members, communicating technical details and progress.
- Support the AI apprentice, helping to build a culture of innovation and knowledge sharing.
What we're looking for
- 3+ years in software engineering with strong experience in building and deploying AI/ML systems, including Retrieval-Augmented Generation (RAG).
- Strong, hands-on experience with scalable microservice architectures, including Kubernetes, Docker, and API gateways.
- Practical experience working with hybrid database solutions for AI, including vector, graph, and relational databases.
- Strong Python skills and practical experience applying AI/ML frameworks for tasks like text-splitting and embedding.
- Experience building and maintaining data ingestion pipelines and agentic workflows.
- Familiarity with identity and access management solutions and their integration.
- Solid experience with MLOps tools and practices, including CI/CD and performance monitoring.
- Experience with key parts of the LLM lifecycle, such as fine-tuning and deployment.
- Degree in Computer Science or a related technical/quantitative field.
- Strong communication skills to collaborate effectively within a technical team.
- 3+ years of hands-on experience in a software or AI engineering role.
- Proven experience building, deploying, and maintaining complex software applications.
Tags & focus areas
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