Artificial Intelligence Jobs

Databricks · United Kingdom

Customer-facing technical authority on Generative AI, LLMs and applied ML for digital-native and startup accounts across EMEA. Shapes architectures, builds hands-on proofs of concept and reference implementations on Databricks (Mosaic AI, MLflow, Model Serving, Vector Search, Unity Catalog), and leads fine-tuning and evaluation engagements on open models. Also mentors the field engineering organisation and represents the company externally through talks and blogs.

Databricks · London

Expert-level technical leader owning the most complex enterprise data and AI engagements for banking and financial services customers. Drives consumption strategy, executive relationships and multi-workload architecture across lakehouse, streaming and ML platforms, while mentoring junior architects and leading competitive displacements.

Experis · London · £70,000 – £110,000 pa

Hands-on leadership role taking AI solutions from proof of concept to secure, production-grade deployments in regulated Security & Justice environments. Involves designing end-to-end AI architectures, leading multidisciplinary teams, shaping technical roadmaps and ensuring governance and security compliance across LLM, RAG and MLOps workstreams.

PhysicsX · New York, United States · US$150,000 – US$250,000 pa

Customer-facing role owning the full customer journey, from onboarding and change management to roadmap planning and contract preparation. Works alongside simulation, software, ML engineers and data scientists to translate engineering and physics challenges into product opportunities, tracking engagement and measuring value. Based in Manhattan with hybrid remote flexibility.

PhysicsX · New York, United States

Lead the ML approach to manufacturing and MRO data, taking models from design through deployment in customer production environments across cloud, on-prem and edge. Work with sparse, drifting sensor and process data, fusing physics simulation with shop-floor measurement, and set uncertainty quantification and validation standards. Mentor engineers and data scientists while travelling to customer sites roughly 3-4 weeks per quarter.

ECM Selection · Cambridge · £40,000 – £70,000 pa

This role involves developing end-to-end machine learning systems integrated with advanced electronics for defence and security applications. You'll work across diverse domains such as computer vision and generative models, building functional prototypes and demonstrators. The role emphasizes innovation, rapid iteration across projects, and close collaboration in a technically driven, low-management environment.

University of Cambridge · Cambridge · £63,606 – £67,468 pa

The role involves contributing to research and teaching in artificial intelligence and machine learning, with a focus on fundamental ML, novel models and architectures. The appointee will develop graduate content for the MPhil in Advanced Computer Science, contribute to undergraduate teaching, and build a research team supported by external funding.

Reperio Human Capital · Dublin, Ireland · £388 – £474 pd

Hands-on engineering role owning AI/ML initiatives end-to-end, from solution design and experimentation through deployment, monitoring and optimisation. Involves building production ML and GenAI solutions including RAG architectures, working with Snowflake and cloud data platforms, and developing Python and R software with strong MLOps, CI/CD and DevSecOps practices. Collaborates with data, product and business teams to turn ambiguous problems into scalable, secure solutions.

Spencer Rose · Bristol · £450 – £480 pd

Designs, builds and deploys end-to-end AI/ML solutions for a banking client's Coach AI programme, including LLM-powered assistants, copilots and multi-agent agentic systems. Involves production deployment, MLOps pipelines, NLP, computer vision and voice AI, plus AI guardrails, responsible-AI governance and mentoring a team as senior lead.

Wayve · London

Owns the engineering release process for autonomous driving models, defining branching strategies, release cadence, configuration management and quality gates. Reviews model, architecture and code changes against intended outcomes, assesses evaluation results and builds CI/CD tooling and observability to deliver ML systems reliably at scale.

DCV Technologies · Manchester · £550 – £600 pd

Contract role building and deploying production AI/ML systems, with hands-on work in Python, PyTorch and Google Cloud Platform. The engineer develops Generative AI solutions using transformer architectures, RAG and agent frameworks like ReAct, then packages and serves models via Docker, KServe and Vertex AI endpoints. Responsibilities also cover MLOps practices such as CI/CD pipelines, model registries, monitoring, drift detection and GPU cost optimisation.

Harnham - Data and Analytics Recruitment · Abingdon · US$200,000 – US$225,000 pa

Build scalable data platforms, pipelines and ML infrastructure, and deploy production AI solutions including LLMs, RAG systems and AI agents. Work in a small, highly visible team partnering with investment and operational stakeholders, owning projects end-to-end from design through production and driving architecture decisions.

Wayve · London

Owns the architecture, strategy and integrity of safety cases for robotaxi and OEM ADAS products, defining top-level claims, argumentation structures and evidence traceability. Sits independently from Engineering and Product, providing expert challenge and oversight across AI/ML, systems, validation and operational safety. Requires deep safety-case and systems engineering expertise in safety-critical domains.

Hadean · London

Lead a new AI-powered defence product team, line-managing AI/ML, simulation, infrastructure and web engineers while staying hands-on in TypeScript, React and Node. You own delivery end-to-end, hiring and people development, partnering with Product and technical leads, with hybrid working one day a week in Shoreditch.

Anthropic · London · £260,000 – £630,000 pa

Own critical parts of a production pretraining pipeline, covering performance optimization, observability, reliability and hardware debugging across the full training stack. The role sits at the boundary between research and engineering, with on-call incident response during model launches and close collaboration with teams in SF and London. It is unusually operational for a research engineering position, offering hands-on work with some of the largest training runs in the industry.