Top AI Jobs in Bristol

Support and enhance ADI's global compliance program aligned to enterprise risk and ISO 37301. Monitor regulatory developments, update policies, execute risk‑based monitoring and testing, manage investigations and remediation, support audits, develop training and communications, track metrics, and collaborate with Legal, HR, Finance, IT, and Operations to drive continuous improvement.

Lead and support execution of ADI's global compliance programs: risk‑based monitoring, policy management, testing, investigations, training, ISO 37301 alignment, audits, remediation, and cross‑functional stakeholder coordination.

Deliver and operationalize AI learning and adoption programs across the firm: run workshops, cohorts, office hours, champion activities, learner support, stakeholder engagement, learning operations, and measurement to embed responsible AI into workflows and drive adoption.

Lead design and delivery of an enterprise AI learning and adoption system. Build role-based learning pathways, deployment playbooks, prompt resources, champion and builder enablement, manager toolkits, and measurement frameworks. Partner with IT, Risk, Legal, Compliance and business leaders to ensure safe, compliant use of approved AI tools and embed learning into onboarding, platforms and workflows to drive measurable adoption and impact.

As a Curriculum Manager, you will manage content development for the data science and engineering curriculum, collaborating with instructors and teams to enhance learning experiences.

Manage curriculum development for data science and engineering, collaborate with instructors, create content, and drive improvements based on learner feedback.

Own and improve Primer's GTM intelligence: maintain dashboards and semantic models, train BI/LLM models (Omni/Claude), surface pipeline/account signals, ensure correct Salesforce modeling, build revenue-driving models and GTM AI workflows, partner with data engineering and RevOps, drive adoption, and promote self-serve data literacy.

Lead technology and AI strategy for the AI Business Solutions practice, focusing on Dynamics 365, Power Platform and Microsoft AI. Define architecture, governance, repeatable Copilot/agent patterns, and technical standards; engage C-suite customers and Microsoft; drive capability investment, IP/accelerators, and senior technical capability development to scale commercially viable AI solutions.

Lead AI architecture for an agentic LLM assistant (Protege) in PatentSight+. Define AI architecture, evaluate models and platforms, design retrieval/vector search and RAG pipelines, author ADRs, establish prompt engineering and evaluation frameworks, ensure security/privacy/compliance, and partner with product, data, and platform teams to deliver production-grade AI systems.

Lead design, implementation, and production operation of agentic LLM systems for medical imaging clinical trials. Build end-to-end agentic workflows (planning, retrieval, tool use), RAG solutions, evaluation frameworks, scalable backend services, and MLOps pipelines. Provide technical leadership, mentor engineers, ensure observability, compliance, and safety, and translate clinical workflows into validated, production-grade AI systems.

Design, build, and operationalize agentic AI systems for clinical imaging trials: intake/validation (DICOM/non‑DICOM), RAG-grounded workflows, QC checks, evaluation harnesses, MLOps deployment, observability, safety/traceability, and cross-functional enablement across the PDLC.

Design and build core backend systems for an agent platform: orchestration, durable execution, memory, tool integration, observability, and evaluation. Create scalable services/APIs, develop retrieval and long-term memory solutions, monitor and improve agent behavior, and help drive technical direction and team growth for AI-driven collaboration features.

Lead and grow a team of AI Solution Architects to design, govern, and operationalize enterprise-scale AI and conversational solutions. Define AI architecture strategy, standards, and governance; advise customers and executives; support sales and product direction; collaborate cross-functionally to build reusable AI frameworks; and drive responsible AI, observability, reliability, and adoption to deliver measurable business outcomes.

Senior Director analyst providing thought leadership and actionable research on infrastructure and operations AI strategy. Produce insights, presentations, and client advisories; guide AI infrastructure, cloud adoption, integration, governance, and vendor selection. Lead research agenda, mentor analysts, collaborate cross-functionally (infrastructure, engineering, enterprise architecture, cybersecurity), support sales, and represent Gartner at events. Drive process improvements and influence senior stakeholders to align infrastructure and operations with business outcomes.

Design polished, user-centric mobile and web interfaces for BJAK's AI and financial products. Create wireframes, flows, prototypes and high-fidelity UI, evolve the design system, collaborate with PMs and engineers, iterate using user feedback and analytics, and ensure accessibility, responsiveness and visual consistency.

Lead development of a generative AI proof-of-concept and integrate AI solutions into software workflows. Mentor data engineers, design and implement Python-based generative AI applications, build scalable data pipelines, deploy models/APIs, and translate stakeholder requirements into technical solutions while staying current on AI and data engineering best practices.

Senior individual contributor researching how threat actors weaponize AI across the attack lifecycle. Instrument telemetry, analyze global data and OSINT, develop detections, automate research workflows with LLMs and scripting, and publish external threat intelligence to inform protections and operations.

Lead go-to-market strategy and commercial execution for a new AI-native Market Research service. Build positioning, pricing, and metrics; generate pipeline and close deals; partner with Sales, Marketing, Product, and Research; represent Maze publicly; and help scale the service and team while feeding market insights back into product and offering design.

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Define and build Fuse's inference-serving architecture and stack, including request routing, batching, scheduling, and autoscaling for latency-sensitive workloads. Own model-level optimizations (quantisation, distillation, speculative decoding), translate performance SLAs into capacity plans, and integrate low-level GPU/CUDA performance work with serving infrastructure. Set benchmarks, tooling, and act as technical owner of inference performance and reliability.

Deploy and maintain AI enablement infrastructure (MCP servers, AI agents, hosting) and cloud environments on AWS/GCP. Build IaC and CI/CD pipelines, monitor performance/cost, support containerized services, manage API keys and permissions, document runbooks, and collaborate with Security, Engineering, and stakeholders to improve cloud governance and AI tooling adoption.

Build and evolve a real-time AI tutoring system: design prompt architectures and agentic workflows, run experiments and custom evaluations, integrate models with platform infrastructure, prototype production model interactions, ensure safety and observability, and collaborate with educators and product teams to translate pedagogy into technical solutions.

Lead the customer-facing conversational agent across chat and voice: own roadmap, design conversations and interfaces in Figma, build prototypes, define agent behavior and quality metrics, partner with enterprise clients and implementation teams, and iterate from production conversation data to improve resolution, containment, and escalation.

Lead and develop a team of Software Development Solution Engineers to drive enterprise adoption of Azure and GitHub developer platforms, GitHub Copilot, and security/DevOps practices. Coach technical presales, build reusable plays and demos, engage senior technical decision-makers, inspect pipeline execution, resolve escalations, and align product and go-to-market strategies to achieve measurable adoption and developer productivity outcomes.

Review, evaluate, and annotate AI-generated content (text, audio, images, video). Identify errors and quality issues, select best outputs, provide written feedback, follow detailed guidelines, attend trainings, and complete tasks remotely within deadlines.