AI Jobs in United Kingdom

View detail

Design and develop robust, scalable, and secure generative AI services and applications using Python and modern frameworks to drive enterprise-wide transformation. Build and optimize high-performance, low-latency APIs and microservices for integrating advanced AI models and agentic workflows into the platform. Collaborate closely with product managers, data scientists, and cross-functional engineering teams to translate complex business needs into innovative AI solutions, from concept to production. Implement and maintain responsive user interfaces primarily focused on backend enablement though some frontend interaction is expected using technologies like React and TypeScript to deliver intuitive user experiences. Partner with DevOps teams to build continuous deployment, logging, and monitoring systems ensuring top-tier performance and reliability. Own key architectural components, ensuring best practices in code quality, security, and maintainability through rigorous testing and peer reviews.

View detail

Define and implement comprehensive quality assurance strategies and test plans for AI agents and LLM-powered applications to ensure product reliability and performance. Design and develop automation frameworks, creating robust, scalable, and maintainable automated test frameworks from scratch or improving existing ones using languages like Typescript, Python, or Scala. Collaborate with product managers, machine learning engineers, and data scientists to understand AI features and model behaviors, and translate them into effective test cases and validation criteria. Drive continuous improvement of testing processes and infrastructure by integrating automated checks within CI/CD pipelines to ensure rapid, high-quality releases. Identify, document, and track software defects and inconsistencies, perform root cause analysis, and provide actionable feedback to development teams. Monitor production systems and AI model performance, proactively identify potential issues, and contribute to post-release quality validation. Champion quality best practices across engineering teams to foster a culture of ownership and continuous improvement in delivering AI solutions.

View detail

The Chief Engineer at Shield AI is responsible for solving complex technical challenges in deploying advanced autonomy solutions on Unmanned Aircraft Systems (UAS). They serve as the chief authority on system architecture, design, development, risk mitigation, and product quality to ensure successful integration of Hivemind Autonomy across various aircraft. This role involves leading a team of engineers to deliver autonomous capabilities for business-to-business and defense contracts. Responsibilities include serving as the Chief Engineer on projects focused on autonomy solutions for unmanned aircraft, leading a team to advance Hivemind Autonomy and define DoD autonomy architectures, assigning technical objectives, making key engineering decisions, ensuring quality and completeness of technical deliverables, providing technical leadership on both IRAD initiatives and DoD contracts, and contributing to government contract proposal writing.

View detail

Design and ship production systems around models, including owning integrations, data provenance, reliability, and on-call readiness across research, clinical, and operational workflows. Lead discovery and scoping from pre-sales through post-sales by translating ambiguous workflow needs into hypothesis-driven problem framing, system requirements, and execution plans with measurable endpoints. Define and enforce launch criteria for regulated contexts such as validation evidence, audit readiness, and outcome metrics, and drive delivery until sustained production impact is demonstrated. Build in sensitive scientific data environments where auditability, validation, and access controls influence architecture, operating procedures, and failure handling. Run evaluation loops that measure model and system quality against workflow-specific scientific benchmarks and use the results to drive model and product changes. Distill deployment learnings into hardened primitives, reference architectures, validation templates, and benchmark harnesses that scale across regulated life sciences environments.

View detail

Set the technical direction and oversee delivery of high-risk, ill-defined software and infrastructure projects, balancing strategic trade-offs and helping teams prioritize in shifting environments, taking full ownership of successful outcomes for challenging projects. Design and develop reliable, production-grade machine learning systems and justify critical architectural decisions to ensure robust delivery. Develop clear, comprehensively scoped roadmaps for novel solutions to help customers achieve strategic goals and accurately estimate effort on large workstreams for timely delivery. Engage with technical and non-technical customers at all stages of the customer lifecycle, providing reasoned and credible advice and opinions on a broad range of engineering topics. Collaborate proactively within multidisciplinary delivery teams and across the engineering community to overcome technical challenges. Coach team members on specific technologies and drive the development of shared organisational resources and libraries to streamline delivery and improve engineering methods across the company. Lead the hiring and selection process and mentor multiple individuals and managers to define the future shape of the engineering team.

View detail

Help users discover and master the Dataiku platform through user training, office hours, demos, and ongoing consultative support. Analyse and investigate various kinds of data and machine learning applications across industries and use cases. Provide strategic input to the customer and account teams that help our customers achieve success. Scope and co-develop production-level data science projects with our customers. Mentor and help educate data scientists and other customer team members to aid in career development and growth.

View detail

The AI / ML Solutions Engineer at Anyscale is responsible for designing, implementing, and scaling machine learning and AI workloads using Ray and Anyscale directly with customers. This includes implementing production AI / ML workloads such as distributed model training, scalable inference and serving, and data preprocessing and feature pipelines. The role involves working hands-on with customer codebases to refactor or adapt existing workloads to Ray. The engineer advises customers on ML system architecture including application design for distributed execution, resource management and scaling strategies, and reliability, fault tolerance, and performance tuning. They guide customers through architectural and operational changes needed to adopt Ray and Anyscale effectively. Additionally, the engineer partners with customer MLE and MLOps teams to integrate Ray into existing platforms and workflows, supports CI/CD, monitoring, retraining, and operational best practices, and helps customers transition from experimentation to production-grade ML systems. They also enable customer teams through working sessions, design reviews, training delivery, and hands-on guidance, contribute feedback to product, engineering, and education teams, and help develop reference architectures, examples, and best practices based on real customer use cases.

View detail

The Lead Machine Learning Engineer will own the development and improvement of the system predicting the next action salespeople should take to advance their relationships. Responsibilities include selecting the best model architecture and approach, involving a mixture of LLM steps and traditional ML models, picking evaluation metrics, designing systems to analyze models in production to identify areas for improvement, and identifying when to use the human data team for training or validation datasets. The engineer will read relevant research to find the best approach for their use case and, in partnership with the CTO, define how machine learning works with product engineering, model operations, and human data teams and how the team should develop moving forward.

View detail

Set the technical direction for complex machine learning projects, balancing trade-offs and guiding team priorities. Design, implement, and maintain reliable, scalable ML and software systems while justifying key architectural decisions. Define project problems, develop roadmaps, and oversee delivery across multiple workstreams in often ill-defined, high-risk environments. Drive the development of shared resources and libraries across the organisation and guide other engineers in contributing to them. Lead hiring processes, make informed selection decisions, and mentor multiple individuals to foster team growth. Proactively develop and execute recommendations for adopting new technologies and changing ways of working to stay competitive. Act as a technical expert and coach for customers, accurately estimate large workstreams, and defend rationale to stakeholders.

View detail

Work alongside machine learning researchers, engineers, and product managers to bring AI Voices to customers for diverse use cases. Deploy and operate the core machine learning inference workloads for the AI Voices serving pipeline. Introduce new techniques, tools, and architecture to improve performance, latency, throughput, and efficiency of deployed models. Build tools to identify bottlenecks and sources of instability, then design and implement solutions addressing the highest priority issues.