Top AI Jobs in Belfast

The VP Field Marketing is responsible for leading marketing strategy and execution in Europe, focusing on demand generation, brand awareness, and pipeline development while collaborating cross-functionally and driving a data-driven culture.

Design and implement large-scale AI/ML systems using LLMs and RAG. Develop data pipelines, optimize models, and collaborate with teams on actionable intelligence solutions.

As an AI Transformation Owner at GitLab, you will shape AI strategies, improve workflows, and drive adoption of AI solutions in your organization, working closely with an AI Engineer.

Manage a cross-functional engineering team building GitLab Duo Chat's AI-powered features. Define requirements with product and UX, contribute to architecture for scalable agentic AI, drive delivery and roadmap execution, improve testing, observability, performance, and coach engineers to foster an inclusive, high-performing team.

The role involves leading insights into AI strategies for legal departments, providing unique research and guidance, and engaging with top legal executives on technology adoption and governance.

Lead research and deliver executive-level insights on enterprise AI strategy, producing thought leadership, advising clients and vendors, supporting sales and conferences, mentoring analysts, and driving research agenda and process improvements to accelerate clients' AI value realization.

As a Staff Backend Engineer at GitLab, you'll enhance backend infrastructure for CI, integrate AI features, and ensure reliability and performance in CI/CD workflows.

Lead R&D stakeholder discovery to define a digital strategy, prioritise initiatives using desirability/viability/feasibility, build a value-linked roadmap, and produce decision-ready recommendations for pilots and transformation.

Support engagements through stakeholder interviews, analysis, synthesis, and document production. Maintain trackers, perform maturity and dependency assessments, draft slide content and executive-ready materials, and assist the Engagement Lead to ensure high-quality, timely strategy deliverables.

Assess current and target R&D technology landscape, define architecture principles for data and AI, identify integration and PLM dependencies and risks, score technical feasibility, and translate findings into executive recommendations to support roadmap delivery.

Manage the Onboarding Risk team across regions, ensuring high-quality risk decisions, developing team members, and owning onboarding risk procedures. Support complex escalations, collaborate cross-functionally, and keep processes compliant with card scheme rules and payments legislation while improving KPIs and decision frameworks.

Ownership of backend features for Agentic Tools: design and implement GraphQL/REST APIs, build secure scalable Ruby on Rails services, improve RSpec automated tests, collaborate across product and AI teams, participate in Tier 2 on-call, and shape architecture for AI agent interactions with GitLab.

Build, ship, and maintain backend features enabling AI agents to interact with GitLab. Design GraphQL/REST APIs, extend tests (RSpec), work with PostgreSQL, troubleshoot production issues, and collaborate cross-functionally to integrate AI tooling responsibly.

Lead and scale an AI Security incubation team to drive technical GTM strategy, recruit and mentor principal AI security specialists, enable global sales with repeatable POVs and playbooks, advise Fortune 500 C-levels on safe AI deployments, and feed field insights into Product and Engineering to drive revenue growth.

Lead field-facing AI security strategy for enterprise customers by designing secure LLM/RAG architectures, delivering demos and POVs, advising on data protection and unsanctioned AI usage, and scaling enablement and product feedback across sales, product, and engineering teams.

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.

As a Senior Product Manager, you will define and evolve GitLab's AI Platform Management capabilities, focusing on AI Control Plane and strategic governance. You'll lead product discovery, drive stakeholder alignment, and collaborate across teams to deliver effective management solutions for AI usage.

As an AI Subject Matter Expert, you will guide the application of AI in core DevOps, partnering with stakeholders to enhance developer productivity and software delivery.

Drive independent, high-impact research on frontier AI topics, publish findings, collaborate widely, and mentor new researchers in the field.

Lead product strategy and roadmap for Actonomy's AI product suite, translate market and customer needs into requirements, support agile delivery, run beta programs, work with engineering and partners (notably Microsoft), define pricing and go-to-market, and manage and develop a small team.

Drive innovation in post-training methodologies for AI models, focusing on agentic behaviors, tool use, and enhancing model capabilities for real-world applications.

As an AI Research Engineer, you'll optimize model deployment and inference architectures for AI systems, enhancing performance across various applications, especially on resource-constrained devices. Responsibilities include designing efficient serving architectures, conducting inference tests, and collaborating with teams to integrate solutions.

As an AI Research Engineer, you will innovate in model compression techniques for large multimodal AI systems, focusing on efficiency and model deployment.

As an AI Research Engineer, you will develop multi-modal reinforcement learning systems, focusing on optimization across data modalities, conducting research on algorithms, and building infrastructure for scalable training and evaluation.