Artificial Intelligence Jobs

Databricks · London

Hybrid technical and commercial role acting as post-sale technical lead across all Databricks products for strategic accounts. You own the technical account strategy and execution plan, driving use cases from win to production, coordinating internal teams, and reporting on usage and customer health. Requires Python or Spark programming and customer-facing architecture or consulting experience.

Databricks · London

Customer-facing engineering role owning architecture and design decisions while building production-grade data, AI and application systems on the Databricks platform. You embed with client teams, from technical ICs to executives, to scope and deliver end-to-end solutions, contributing reusable accelerators and product feedback. Requires hands-on coding in Python, Scala or TypeScript, deep Apache Spark and distributed computing expertise, and multi-cloud knowledge, with around 20% travel to customer sites.

Palo Alto Networks · Paris, France

Designs and plans Idira privileged-account security solutions for customers, coordinating with implementation, sales and product teams to ensure architectures meet standards and are executed as planned. Acts as a technical expert advising on best practices, risk and business impact while managing the full customer journey and escalations.

NVIDIA · Reading

Acts as the primary technical contact for academic and research partners, embedding NVIDIA's frameworks and software stack into large-scale AI and HPC workloads. Drives joint research projects, delivers workshops and trainings, and feeds researcher needs back to engineering. Requires deep LLM lifecycle expertise across multi-node systems and involves around 30% travel.

NVIDIA · France

Defines AI inference technical direction across EMEA, leading engagements with frontier AI labs and enterprises from proof of concept to production-scale deployment. Architects and optimizes high-performance inference pipelines using NVIDIA Dynamo, TensorRT-LLM, vLLM and SGLang, tackling latency, cost per token, memory and networking bottlenecks while feeding deployment insights back into the product roadmap.

PhysicsX · London

Builds the simulation side of Large Physics Models, generating multi-fidelity CFD data to train large-scale ML models. Develops solvers and numerical methods in differentiable or GPU-native frameworks such as JAX or NVIDIA Warp, using commercial and open-source CFD tools for reference data. Works in a research team alongside ML researchers, shaping data generation strategy and fidelity tradeoffs.

PhysicsX · San Francisco, United States · US$150,000 – US$190,000 pa

Builds and deploys machine learning pipelines for industrial physics applications, working directly with customers on-site to embed AI models into practical tools. Involves manipulating 3D point-cloud and mesh data, cloud and HPC infrastructure, and translating R&D outputs into reusable libraries and products.

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.

eFinancialCareers · London

Leads the design, development and operation of high-rigor analytical and machine-learning systems across a complex, regulated financial-services estate, defining the AI/ML roadmap while remaining hands-on. Owns the full model lifecycle from feature engineering and back-testing to deployment, monitoring and incident response, with models expected to be explainable and production-grade. Works closely with engineering, data platform and product teams, mentoring other data scientists with limited line management.

eFinancialCareers · London

Design and build AI-powered tools, automations and integrations using Python, PowerShell and APIs, exploring Agentic AI and LLM use cases across internal business functions. Advise on embedding AI into enterprise architecture within a regulated investment management environment, working with engineering, product and business teams. Distinct for its focus on engineering productivity and architecture strategy rather than traditional ML modelling.

Latent Labs · London

Architect novel generative models to design functional proteins, working in an interdisciplinary team of ML researchers, protein engineers and biologists. The role spans data curation, evaluation metrics, rapid prototyping, cloud training and inference, and planning wet lab testing campaigns, with about 10% of time for self-development.

NXA Talent LTD · London · £500 pd

Design and build agentic AI systems with multi-step reasoning, multi-agent orchestration and scalable AI microservices in Python and Azure OpenAI. Develop RAG architectures, vector search and orchestration workflows using Semantic Kernel and LangGraph, integrating enterprise systems via MCP. The role also covers AI governance, safety and deploying LLM services into production within a large retail environment.

Scale AI · London

Forward-deployed full-stack engineer who partners directly with public sector clients to turn ambiguous mission requirements into production-grade AI applications. Leads architecture across React, Node.js, Python, Kubernetes and global cloud infrastructure, mentoring staff and shaping reusable platform patterns. Founding-member role on Scale's Global Public Sector team.

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.

Latent Labs · London

Architect novel generative models to design functional proteins, working in an interdisciplinary team of ML researchers, protein engineers and biologists. The role spans data curation, evaluation metrics, rapid prototyping, cloud training/inference optimisation and planning wet lab testing campaigns, with about 10% of time for self-development.