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.
Develop and deploy agentic AI and LLM-based applications within a cloud-native environment, focusing on intelligent agent systems and AI platforms. Work closely with scientific teams to support cutting-edge research through scalable backend services and MLOps infrastructure. The role involves strong ownership of development, deployment, and observability in production environments.
The role involves software testing, software releases, and customer support for an AI startup. You will work with Python, Bash, Linux, Docker, and AWS, and have the opportunity to develop skills in automation, cloud, and edge technologies in a fast-moving, collaborative environment.
The Senior Generative AI Architect will design, develop, and implement advanced generative AI solutions, ensuring scalability, security, and ethical considerations. They will lead the architecture and deployment of AI models, collaborate with cross-functional teams, and stay updated on the latest AI advancements.
This role involves designing, building, and integrating AI applications into end-to-end workflows, embedding agentic AI into customer and operational processes, and collaborating with cross-functional teams to deliver robust, production-ready solutions. The focus is on turning emerging AI capabilities into practical, high-value implementations while ensuring reliability, resilience, and safety.
This role involves designing and implementing technical evaluation frameworks for AI systems, with a focus on model performance, safety, and responsible AI. The engineer will develop automated testing pipelines, translate policy and risk considerations into measurable benchmarks, and report findings to stakeholders. Work takes place in a government context using cloud platforms and agile practices, with hybrid attendance in London, Bristol, or Manchester.
This role involves designing and delivering secure CI/CD pipelines, building and supporting Kubernetes/container platforms, and embedding security controls into engineering workflows. You'll work closely with data and AI teams to improve platform reliability and deployment standards, with a focus on DevSecOps and automation.
This role involves designing and implementing data architecture using Snowflake, AWS, and AI/ML technologies. Responsibilities include defining data modeling standards, securing data pipelines, and optimizing Snowflake performance. The position also focuses on cross-cloud orchestration, data governance, and preparing the data infrastructure for AI applications.
This role involves supporting the development and deployment of AI-driven automation solutions, including chatbots and virtual assistants, using tools like Microsoft Copilot Studio. The engineer will collaborate across teams to identify AI opportunities, test emerging technologies, and help implement practical AI applications. It is a hands-on position focused on learning, experimentation, and applying AI to real-world business processes.
This role involves designing, building, and deploying high-performance Machine Learning solutions, working across the full ML lifecycle from experimentation to production. You'll collaborate with engineering and AI teams to ensure robust MLOps practices and optimize model performance in production environments.
Design and optimize machine learning models for hardware acceleration on platforms such as GPUs and QPUs, focusing on low-latency distributed systems and simulation. Work independently within an established team to support international clients using cutting-edge technologies. The role involves algorithm development in C++, Python, or Rust, with a strong emphasis on performance and hardware integration.
This role involves developing and scaling a generative AI platform, with ownership of end-to-end feature development across backend, frontend, and AI components. The engineer will work closely with ML models and LLMs, build reliable distributed systems, and integrate with vector databases and AI infrastructure. Collaboration with product, ML, and infrastructure teams is key, in a fast-paced environment focused on delivering enterprise-grade AI solutions.
This role involves designing, building, and deploying agentic AI systems in a production environment. You will develop AI-driven workflows for decision-making in complex, rules-based systems, integrate LLM-powered systems, and work with large-scale structured datasets. The position offers high ownership and the chance to shape AI applications in critical, data-intensive domains.
The Data Scientist will develop and implement machine learning models to solve business problems, analyse large datasets for actionable insights, and collaborate with technical and business stakeholders to deliver data-driven solutions. The role involves preparing, cleaning, and transforming data, as well as evaluating and monitoring models for accuracy and reliability.
This role involves designing and building features across the full stack, working with modern technologies like React, TypeScript, Python, Node.js, PostgreSQL, and AWS serverless/microservices. You will shape the technical architecture, mentor a junior developer, and contribute to a cloud-native platform in an AI-first environment.
