Top Tech Jobs & Startup Jobs in Dublin

Design and deliver production-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and microservices on cloud platforms (AWS, Cloudera, Databricks). Use frameworks like LangChain and MCP, ensure security/compliance, monitor model performance, and mentor junior engineers while collaborating across product, engineering, and data teams.

Lead hands-on engineering for agentic AI products and platforms: define architecture and vision, build scalable agent frameworks, partner with Product/Data/Security, ensure production-grade reliability, observability, safety, and developer tooling to accelerate enterprise AI adoption.

Lead engineering for agentic AI products and platforms: define vision, build scalable agent frameworks, deliver production-grade LLM-powered systems, drive AI tooling adoption, ensure security/governance, and partner cross-functionally to operationalize agentic capabilities.

Lead creation of a Support and Retention Experience function to monitor customer health, build post-production support, automate playbooks, align cross-functional data, identify expansion opportunities, and drive retention and platform adoption.

Own the post-enablement customer journey for live production customers: monitor API performance and health, operate proactive alerting, define customer health scores and playbooks, coordinate issue resolution across SRE/engineering/product, surface in-platform guidance, identify expansion opportunities, and provide data-driven reporting to leadership to improve retention and platform adoption.

Own the post-enablement experience for live production customers: monitor API performance and health, build health scores and alerting, maintain playbooks, coordinate cross-functional issue resolution, surface in-platform guidance, identify expansion opportunities, and provide leadership with data-driven visibility to improve retention and adoption.

Design, build, and deploy enterprise AI solutions using LLMs, RAG, and vector search. Architect scalable, secure cloud-based systems (AWS, Databricks, Cloudera), develop APIs and microservices, optimize model performance, and ensure data governance and compliance. Mentor engineers and collaborate cross-functionally to deliver production-ready AI applications.

Lead architecture, design, and development of a cloud-native experimentation platform. Drive scalable microservices, APIs, and big-data pipelines (Databricks/Spark), ensure performance, security, reliability, mentor engineers, and coordinate cross-functional/global teams to deliver enterprise-grade analytics products.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable, secure cloud-native services and APIs, optimize model performance in production, mentor engineers, and enforce data governance and compliance.

Lead a team to design, build, and deliver resilient, scalable API-driven microservices. Oversee project delivery, code reviews, SDLC best practices, testing (unit/integration/e2e), security, performance engineering, architecture decisions, and people management including hiring, coaching, and performance reviews.