Top AI Jobs in Bristol

Owns roadmap execution and capability definition for an Agentic AI Platform. Translates AI platform strategy into requirements, prioritized roadmaps, reusable agent orchestration and lifecycle capabilities, governance controls, observability, and scalable platform patterns. Partners with engineering, architecture, data, security, legal, compliance, customer success, professional services, and customers. Measures adoption, reliability, safety, quality, cost, and business impact while balancing technical feasibility, security, compliance, and customer value.

Own the production improvement loop for an AI Context Engine, improving agent behavior, retrieval, memory, telemetry, evaluation, and safe rollout. Build production services in TypeScript or Python, design observability and evaluation systems, analyze agent traces and customer feedback, and partner with data scientists, backend engineers, product, and UX. Mentor engineers, review designs, and shape the technical roadmap for agentic products with public APIs and evolving data models.

Owns revenue for Cohesity’s AI business unit across product, infrastructure, and consulting services. The role generates pipeline, sells AI solutions to executive buyers, develops account plans, delivers business cases, manages enterprise opportunities, and coordinates co-selling with partners such as NVIDIA and hyperscalers. Responsibilities also include CRM hygiene, forecasting, competitive analysis, and building multi-phase customer engagements.

Lead product direction, roadmaps, standards, metrics, and execution for major areas of KIRA’s AI stockbroking app. Guide product managers, product owners, designers, and engineers across multiple squads; balance UX, data, engineering, regulatory, operational, and business considerations; drive launch quality and continuous improvement; and build a high-performing product culture focused on speed, clarity, user value, and measurable outcomes.

Lead product direction, roadmap, standards, and success metrics for major areas of KIRA’s AI neobank app. Guide product managers, product owners, designers, and engineers across multiple squads and financial services, including insurance, payments, savings, investing, and travel. Make product decisions amid regulatory and operational constraints, drive launch quality, measure outcomes, and foster a fast, user-focused product culture.

Responsible for leading post-silicon validation for AI SoCs, ensuring reliability and quality of new silicon. Collaborates across teams to resolve issues and improve validation processes.

Owns the full data-product lifecycle, including retail data imports, integrations, pipelines, metrics, reporting APIs, AI agents, and dashboards. Sets roadmap priorities, conducts retailer discovery, partners with engineering and data teams, validates product adoption, and uses SQL and analytics to measure business impact. Requires strong technical depth, enterprise customer communication, cross-functional execution, and occasional travel for store and customer visits.

Lead consulting, discovery, advisory, and shaping engagements for Microsoft Business Applications, Power Platform, Copilot, and AI-enabled business processes. Facilitate stakeholder workshops, assess services and processes, define requirements and roadmaps, shape operating models and governance, establish value measures, and guide adoption and organizational change. Collaborate with multidisciplinary teams while advising on responsible AI, privacy, security, regulatory, and human oversight considerations. Contribute to proposals, business development, reusable consulting assets, coaching, and people management.

Own AI stockbroking and other financial-services product areas from user-problem discovery through launch and iteration. Define product direction, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial workflows; use research, feedback, experiments, and performance data to improve products; and balance speed, user experience, business impact, compliance, and operational complexity.

Own the end-to-end product strategy and execution for an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing reliability, latency, cost, safety, and usability. Partner closely with ML, backend, and client engineering teams to identify failure modes, improve system performance, prioritize work, and deliver predictable, trustworthy AI capabilities in a fast-moving zero-to-one environment.

Own the end-to-end product direction and quality of an AI-powered email application. Define AI behavior, system requirements, evaluation frameworks, quality standards, and user experiences while balancing quality, latency, cost, reliability, safety, and usability. Partner closely with ML, backend, and client engineers to identify failure modes, prioritize improvements, run experiments, and deliver predictable, trustworthy AI features in a fast-moving zero-to-one environment.

Lead application engineering for an AI email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI capabilities to product experiences, guide a senior engineering team, make architecture and reliability trade-offs, and establish observability, monitoring, testing, and operational excellence. Partner with ML leadership to ship scalable, maintainable, and high-performing AI-powered experiences.

Own the end-to-end product direction and quality of AI-powered systems. Define requirements, evaluation frameworks, quality bars, and feedback loops while balancing model quality, latency, cost, reliability, safety, and user experience. Partner deeply with ML, backend, and client engineers on system design and iteration, identify failure modes, and drive execution in a fast-moving zero-to-one environment.

Build production-grade agentic AI infrastructure for operational automation, including memory, context management, tool-calling, orchestration, evaluation, observability, and feedback loops. Develop reliable LLM workflows, coding-agent systems, and multi-agent architectures while partnering with operational and product teams to automate complex workflows across acquired agencies. Influence architecture, engineering standards, and reusable AI primitives in a remote, early-stage team.

Lead new business development for CounselLink across the UK, managing the full enterprise sales cycle from prospecting through contract execution. Build executive relationships within corporate legal departments, develop enterprise pipelines, deliver product demonstrations, lead RFP responses, negotiate agreements, forecast sales, and identify cross-sell opportunities. The role requires consultative SaaS selling experience, familiarity with legal technology and the UK legal market, and the ability to influence senior stakeholders in a remote, cross-functional environment.

Lead AI and experience orchestration sales initiatives, supporting account teams with technical expertise, deal strategy, territory planning, customer workshops, proofs of concept, proposals, and executive presentations. Serve as a subject matter expert for AI-powered customer experience and conversational AI solutions, coordinate cross-functional teams, influence opportunity progression, and develop AI-led pathways aligned with customer objectives.

Produces daily 60–90 second AI-generated micro-drama episodes for TikTok, Instagram Reels, and YouTube Shorts. Responsibilities include developing striking visuals, consistent characters, natural voices, audio, purposeful editing, and story-driven visual styles. The role requires prompt engineering, strong editing fundamentals, independent execution, fast delivery, and familiarity with vertical video. Experience in traditional video production, dramaturgy, end-to-end AI video creation, and short-form drama is advantageous.

Leads Colibri’s applied AI capability, setting technical direction, engineering standards, propositions, and team strategy. Guides AI discovery, solution design, client conversations, pre-sales, delivery, and commercial growth. Oversees production AI, architecture, MLOps/LLMOps, testing, evaluation, security, governance, and reusable intellectual property. Mentors senior engineers, develops the practice, builds market presence, and ensures AI solutions are secure, scalable, commercially viable, and production-ready.

Leads complex enterprise customer engagements as a senior technical authority for data and AI architecture. Drives Databricks adoption, consumption strategy, executive relationships, competitive positioning, and large-scale architecture initiatives including migrations, real-time systems, lakehouse platforms, and AI/ML. Orchestrates cross-functional teams, mentors solutions architects, influences senior stakeholders, and contributes to technical thought leadership.

Lead complex, cross-functional Enterprise Technology and AI programs from planning through delivery. Develop roadmaps, resource plans, success metrics, governance frameworks, and status reporting. Align technical and business stakeholders, manage dependencies and risks, resolve blockers, and translate requirements into actionable plans. Partner with IT leadership and functions including Finance, Legal, Sales, and Product in a globally distributed, async-first environment.

Own the function’s AI transformation strategy, prioritize high-impact opportunities, map workflows, and drive measurable adoption. Build and manage AI agents using no-code and low-code tools, configure skills and MCP connections, evaluate agent performance, and iterate solutions. Lead change management, champion networks, cross-functional collaboration, intake processes, and alignment with Enterprise AI and executive stakeholders.

Build and maintain secure, scalable Flow Registry features for GitLab’s agentic platform using Python, LangChain, and LangGraph. Design GraphQL and REST APIs, improve automated testing, optimize PostgreSQL-backed applications, and collaborate across backend, frontend, product, UX, and AI teams. Participate in Tier 2 on-call support, troubleshoot production issues, improve observability, and contribute to responsible AI integration in GitLab’s remote, asynchronous environment.

Lead developer relations across EMEA AI Natives by advising partners, startups, and developers on NVIDIA technologies. Demonstrate AI and computing solutions, create technical enablement resources, guide onboarding and integrations, map ecosystem opportunities, and influence product roadmaps through partner feedback. Collaborate with engineering, product, marketing, sales, and legal teams while delivering technical talks, workshops, mentorship, and strategic developer programs.

Own production NLP and applied AI systems that enrich scientific literature with entities, classifications, claims, and summaries. Design and evaluate classical NLP, embedding, retrieval, and LLM approaches; build scalable Python pipelines; manage cost, concurrency, reliability, and model quality. Collaborate with data engineers on Airflow and Dagster orchestration, contribute to agentic AI applications, and work with editors, product managers, and engineers to deliver research intelligence to users.

Build and operate NLP enrichment pipelines over scientific literature, extracting entities, classifications, claims, and summaries. Compare classical NLP, retrieval, embedding, LLM, and fine-tuned model approaches using evaluations, cost, quality, and operational metrics. Create golden datasets, define evaluation strategies, contribute to agentic AI applications, and collaborate with editors, product managers, engineers, SMEs, and vendors to deliver production modeling systems.