Top Tech Jobs & Startup Jobs in Dublin

Build and ship Kotlin-based native Android features for an AI-enabled neobank. Design clean user flows, integrate backend APIs, improve performance and reliability, use analytics to iterate, and implement on-device AI where it benefits UX.

Run full-cycle recruitment across multiple functions and regions: take briefs, source (LinkedIn, job boards, referrals), screen, interview, manage ATS, build pipelines, close roles quickly, and improve hiring processes and employer brand.

Build end-to-end AI-native product features across frontend, backend, and model integrations. Design and deploy agent workflows, integrate LLMs, memory, and external tools, and ensure real-time streaming, low-latency interactions, reliability, observability, and robust fallback/recovery. Collaborate with ML, backend, and product teams and iterate based on real-world usage.

Own and ship core ML subsystems end-to-end for a proactive, long-horizon AI assistant. Work includes data preparation, training, evaluation, inference, debugging production failures, optimizing for latency/cost/reliability, and mentoring other ML engineers.

Build and ship end-to-end AI features, shaping model behavior and surrounding systems. Design prompts, tools, memory, and agent workflows; convert model outputs into reliable behavior; debug across model, infra, orchestration, and UX; optimize latency, cost, and reliability; and develop evaluation frameworks to measure real-world performance.

Build and maintain a production Android app in Kotlin that integrates AI features (chat, vision, voice). Design AI UX patterns, optimize performance and memory, implement analytics and logging, collaborate with backend and ML teams, and ensure stability, security, and scalability in production.

Build and operate end-to-end ML infrastructure for training, evaluation, deployment, inference, observability, and CI for large-scale AI systems. Optimize reliability, latency, throughput, cost, and developer tooling to enable rapid experimentation and production-grade model serving.

Design, build, and productionize LLM-powered applications and agent workflows. Integrate models with tools, databases, and services; develop prompting, orchestration, evaluation frameworks; debug and optimize AI behavior for reliability, latency, cost, and product impact.

Build and productize AI-native systems for a fintech neobank: AI agents, RAG/LLM-powered workflows, tool integrations, and automations for onboarding, KYC, support, risk, CRM and operations. Focus on production reliability, evaluation, guardrails, latency, cost, security and compliance while collaborating with product and engineering teams.

Build and ship end-to-end product features across frontend, backend, APIs and databases. Create customer/internal workflows (onboarding, accounts, payments, KYC, FX, rewards). Collaborate with product, design, mobile, backend and AI teams. Build internal tools, monitor production, debug and iterate to improve reliability and UX.