Artificial Intelligence Jobs

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.

Databricks · London

Serve as the trusted technical ML and AI expert for enterprise customers, architecting production-grade ML and GenAI workloads on the Databricks platform. The role spans RAG architectures, agentic systems, MLOps lifecycle management, and cloud-native integration, while mentoring colleagues and influencing the product roadmap. It combines deep hands-on engineering with customer-facing technical sales support.

ARM · Dublin, Ireland

Lead the architecture, deployment and operation of a large-scale AI/ML platform spanning conventional ML and agentic AI, owning the roadmap and engineering standards. Hands-on role covering MLOps/LLMOps, CI/CD, IaC, Azure infrastructure and multi-agent orchestration frameworks, while mentoring engineers and acting as senior escalation point.

Databricks · London

Lead the technical strategy for enterprise accounts in media, entertainment and agencies, owning architecture discussions from discovery through production deployment and consumption growth. Design scalable lakehouse, streaming and ML/AI solutions while advising customer architects and engineering leads, with live coding in Python and SQL.

VIQU IT · London · £90,000 – £95,000 pa

Owns the end-to-end architecture of an enterprise AI platform spanning Azure and AWS, covering ML training, model hosting, MLOps and Generative AI. Defines patterns for LLMs, RAG, vector search and agent frameworks, evaluates tooling, and provides technical direction to ML and platform engineers while embedding security, governance and regulatory requirements.

Tenth Revolution Group · London · £95,000 – £125,000 pa

Architect and implement Databricks Lakehouse solutions covering ingestion, processing, storage, governance and analytics for enterprise clients. Lead architectural engagements, tune workloads for performance and cost, and build CI/CD pipelines with Databricks Repos, GitHub Actions and Azure DevOps. Hybrid role in a consultancy, contributing reusable patterns to a wider architecture practice.

Isomorphic Labs · London

Research engineers translate frontier research concepts into practical implementations, building and optimising state-of-the-art AI models such as Transformers, GNNs and diffusion models, plus the codebases, data pipelines and infrastructure for training and evaluation. They design and run experiments, analyse model performance, and advise on bringing models to production, working closely with research scientists in a cross-disciplinary team.

Speechmatics · London

Own the infrastructure behind speech AI models, building scalable data pipelines for ingesting, transforming and validating large training datasets, plus web scraping and data acquisition tooling. Operate the platform that trains, evaluates and serves models in production, optimising GPU utilisation, scheduling and observability across distributed systems.

Speechmatics · Cambridge

Own the infrastructure behind speech AI models: building scalable data pipelines for ingesting, transforming and validating large training datasets, plus web scraping and automated data acquisition. Also operates the platform for training, evaluating and serving models, optimising GPU utilisation, scheduling and observability across distributed systems.

Ncounter · Manchester · £80,000 – £90,000 pa

Hands-on research engineering role investigating the internal behaviour and security of large language models, turning academic ideas into practical experiments in Python. Work spans model internals, data movement, training risk and safe deployment, alongside production-quality ML engineering, testing and code reviews. Requires active SC clearance and around three days per week on secure sites.

Wayve · London

Own end-to-end system architecture for the Wayve AI Driver across L0–L4, defining interface contracts, sensor and compute topology, redundancy and safety mechanisms. Lead technical alignment with OEMs, Tier 1s and partners, and support integration, bring-up and system-level debugging on vehicles and HiL/ViL benches.

Amtis Professional · Birmingham · £80,000 – £90,000 pa

Architect end-to-end enterprise AI solutions spanning data, knowledge and process layers, covering internal processes, reporting, frontline operations and customer experience. Design and validate proof-of-concept solutions, define target architectures and reusable patterns, and lead MLOps and model deployment pipelines for LLMs, agentic and generative AI. Work hybrid in the West Midlands across IT, data, security and business teams to move PoCs toward production.

Amazon · London

Lead end-to-end delivery of complex AI/ML engagements for enterprise customers, from strategic planning through pre-production deployment and optimisation. Architect solutions on AWS services with a focus on Generative AI using Amazon Bedrock and SageMaker, while mentoring junior consultants and supporting pre-sales scoping. Based in AWS UK offices when not at customer sites.

Synthesia · London

Hands-on principal IC role owning the ML platform that lets researchers and product teams train, serve and deploy generative models. Work spans research infrastructure, production serving, internal tooling and agent-oriented automation, with a focus on reliability, scalability and GPU/cloud resource efficiency. Distinct for its systems-generalist framing rather than pure ML engineering.

Helsing · London

Architect and scale the deep learning frameworks and distributed training infrastructure behind defence AI systems, extending PyTorch-based tooling and optimising GPU throughput. The role spans data strategy, storage and orchestration, working cross-functionally to maximise training efficiency and developer velocity.

Entrust · London

Builds and operates production ML systems for document classification and extraction, using LLMs/VLMs and traditional ML to process government identity documents at scale. Owns features from RFC through deployment, optimising GPU inference and training/evaluation pipelines, and mentors engineers within a squad-based team.