LocationRemote working (anywhere in the UK)Salary: £78,000 to £85,000 (including a £10,000 market rate allowance), plus a further £5,000 for candidates based in London.About the jobJob summaryThis post is open to applicants who can meet the College’s nationality, UK residency...
As Head of AI, you will lead the development and implementation of AI strategies, governance, and capabilities at LPPA, a leading pension administration company. You will work closely with the CTO and executive team to identify and deliver AI opportunities that enhance operational efficiency, service quality, and member outcomes. Key responsibilities include building the AI team, establishing governance frameworks, and fostering AI adoption across the organization.
This role involves leading the development of AI-powered features within a legal tech SaaS platform, focusing on NLP, machine learning, and large language models. You will design and implement models for document understanding, search, and workflow automation, ensuring they are performant, reliable, and secure in a production-grade cloud environment.
Design and deploy production-grade NLP systems and large language models tailored for financial services, with a focus on scalability, data governance, and regulated environments. Develop fine-tuned, low-latency AI workflows integrated into cloud infrastructure, while mentoring engineers and advancing evaluation methodologies for LLM outputs. Work across the full ML lifecycle from research to deployment in a remote-first, innovation-driven team.
This role involves owning and developing the MLOps infrastructure for a growing AI platform, building and maintaining orchestration pipelines, deploying ML workloads on Kubernetes, and implementing CI/CD and monitoring systems. You will work closely with Data Scientists and ML Engineers to scale deployments and support real-world impact.
The Senior AI Engineer will work on developing and deploying large language models (LLMs) into a Legal Tech SaaS platform. They will join a high-performing engineering team focused on reliability, performance, and error prevention, using technologies like AWS, Lambda, Hugging Face Transformers, and Python.
This role involves designing and developing AI/ML solutions from concept to production, working with modern AI technologies like LLMs, RAG, NLP, and Computer Vision. You will collaborate with experienced engineers and customers to solve complex real-world problems, focusing on robust Python software development and model optimization.
This role involves designing, building, and scaling enterprise-grade AI platforms, transforming AI research into production systems. You'll work on complex AI services, data pipelines, and cloud-native architectures, collaborating with Data Scientists and Software Engineers to productionize ML and NLP models.
This role involves rapid prototyping and experimentation with cutting-edge AI technologies, including generative AI, LLMs, and AI agents, to solve real business challenges in financial services. The engineer will build intelligent workflows and automations, test solutions with end users, and drive innovation from concept to enterprise-scale deployment. The position emphasizes a hands-on, build-first approach within a high-autonomy innovation lab environment.
This role involves supporting the development and deployment of AI and machine learning models, working with large datasets, and collaborating with software engineers and product teams. You'll contribute to backend development, API integrations, and research new AI frameworks, ensuring responsible and ethical AI development.
This role involves building and maintaining scalable data pipelines, designing data models, and developing machine learning solutions to support AI initiatives and business intelligence. You will work closely with stakeholders to translate business challenges into technical solutions, using tools like Microsoft Fabric, Databricks, and PySpark.
Design and implement end-to-end AI/ML systems using generative AI, LLMs, and agentic frameworks to extract insights from biomedical literature and data. Focus on production-grade reliability, model quality, and collaboration with product and engineering teams to deliver user-facing features. Mentor junior engineers and contribute to a culture of technical excellence and knowledge sharing.
Lead the design and deployment of scalable AI and generative AI solutions, including LLM-based applications for enterprise search, document summarization, and conversational AI. Drive innovation through retrieval-augmented generation (RAG) architectures and mentor engineering teams in transformer models and prompt engineering. Collaborate across data, ML, and business teams to deliver measurable impact using cloud AI platforms and MLOps practices.
This role involves prototyping, building, and implementing new AI approaches, as well as evaluating their quality and efficiency. You'll work in a dynamic R&D team at a leading AI company, focusing on data-driven solutions to AI problems.
Conduct experimental research on AI safety, focusing on alignment and robustness of advanced systems. Develop and test techniques like AI debate and jailbreak evaluation, contributing to publications and safety policy. Work in a collaborative, interdisciplinary team using machine learning and systems engineering.
This role involves designing and building production-grade AI software solutions for enterprise use within a regulated banking environment. The engineer will work on cutting-edge Generative AI applications such as intelligent document processing, fraud detection, and cross-language translation, turning ambiguous problems into scalable, auditable systems. Collaboration with product, risk, and engineering teams is central, with a strong emphasis on resilience, explainability, and technical leadership.
Design and lead the architecture of enterprise-scale AI/ML and Generative AI systems on Google Cloud Platform. Focus on production-grade LLM, Agentic AI, and RAG solutions with strong governance, security, and scalability. Drive technical standards and MLOps practices across the AI lifecycle.
