As a Senior AI Engineer at PoliceAI, you will lead the design, development, and deployment of AI-enabled products, translating policing needs into technical solutions. You will work with multidisciplinary teams to build secure, maintainable, and scalable services, and support the testing, evaluation, and validation of AI products.
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 serving as a technical advisor and champion for the developer ecosystem in Healthcare and Lifesciences, accelerating workloads by integrating NVIDIA’s AI and computing platforms, and fostering co-innovation with partners. Responsibilities include technical enablement, ecosystem mapping, and cross-functional collaboration to drive adoption and solve real-world challenges.
This role involves applying AI, machine learning, and advanced analytics to improve health and care services. You will develop secure and reproducible AI pipelines, analyse complex health datasets, and collaborate with academic, NHS, industry, policy, and public partners to generate insights and support innovation.
This role involves developing and maintaining critical software engineering tools and architecture to drive engineering efficiency, reliability, and continuous improvement. Responsibilities include maintaining AWS resources, enhancing CI/CD pipelines, and providing observability tools to support AI agentic workflows.
Develop and maintain high-performance, secure embedded software for next-generation intra-operative medical devices using modern C++ and edge computing technologies. Collaborate across multidisciplinary teams to enhance device connectivity, real-time decision support, and cloud integration within a regulated medical environment. Focus on code quality, reliability, and compliance with security and regulatory standards.
This role involves researching and developing AI/ML approaches for power electronics applications, designing and simulating power electronic converter systems, and optimizing converter design and performance. You will work closely with experienced engineers and researchers to translate academic research into practical engineering solutions, focusing on sectors like electric vehicles, rail, and renewable energy.
This role involves building production applications and APIs using Python and FastAPI, developing LLM, RAG, and AI agent solutions, and working with vector databases. You'll collaborate with engineers, architects, and client stakeholders to take GenAI POCs into production and scale them.
This PhD project focuses on developing robust, scalable, and certified federated machine unlearning algorithms to ensure privacy and compliance in high-stakes domains like healthcare and finance. The research will explore certified unlearning, robustness to adversarial attacks, and the application of unlearning to large pretrained models, contributing to trustworthy AI.
This PhD project focuses on developing a rigorous empirical framework to assess the security robustness, failure modes, and operational risks of AI decision engines in cyber-physical systems. The research will provide empirical evidence to support informed deployment decisions in safety-critical industries like transportation, energy, and manufacturing.
The role involves providing academic leadership in Digital Health, AI, and Society, with a focus on establishing institutional capability in these areas within medicine. The postholder will also contribute to a university-wide initiative exploring the impact of AI on learning, knowledge creation, and professional development.
The Senior Data Scientist role involves designing and delivering AI/ML models for forecasting, customer segmentation, churn prediction, and commercial optimisation. You will own end-to-end data workflows, operationalise models using ML Ops, and collaborate with product, engineering, and client teams to align technical outputs with business outcomes.
This PhD studentship focuses on advancing Bayesian machine learning methods for precision medicine, specifically in Type 2 diabetes treatment selection. The project will leverage large-scale observational healthcare data to optimize treatment choices, addressing key challenges in model development.
This PhD project focuses on developing an AI-enabled digital twin framework to support circular retrofit decision-making in public buildings. The research will integrate building condition data, asset records, and lifecycle information to identify opportunities for extending component life and reducing waste during refurbishment projects.
This role involves designing, developing, and deploying scalable data pipelines using Azure Databricks, Azure Data Factory, and Azure SQL Database. You will work on high-impact insurance data transformation programs, ensuring data quality, performance, and security, and collaborate with experienced data professionals in a forward-thinking environment.
Lead the design and deployment of enterprise AI and data transformation initiatives in highly regulated sectors, focusing on secure, scalable production systems. Guide technical roadmaps, mentor cross-functional teams, and ensure compliance with governance and security standards. Work across AI/ML, generative AI, and cloud platforms to deliver end-to-end solutions from concept to operation.
This role involves leading technical strategy for customers adopting Databricks' Data + AI Platform, with a focus on data engineering, machine learning, and real-time analytics. The Solutions Architect will design scalable, production-grade solutions, lead architecture discussions with senior technical stakeholders, and act as a trusted advisor through the full customer journey. A key aspect is developing deep expertise in a specific domain such as AI/ML, streaming, or data governance to support both customer success and internal technical leadership.
