Top Python Jobs in Dublin

Lead engineering and deliver production-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Architect scalable cloud solutions (AWS, Cloudera, Databricks), build APIs and microservices, implement MCP-style orchestration, monitor model performance, ensure security and compliance, and mentor engineering teams.

Lead design and deliver enterprise-grade AI solutions using LLMs, RAG, LangChain and vector search. Build and maintain knowledge bases, architect scalable cloud solutions (AWS, Cloudera, Databricks), develop APIs/microservices, ensure security/compliance, monitor model performance, mentor engineers, and collaborate cross-functionally to translate business needs into production AI systems.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and scalable cloud-native systems (AWS, Cloudera, Databricks). Mentor engineers, ensure security/compliance, optimize model performance, and collaborate cross-functionally to productionize AI applications.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, LLM-driven apps (LangChain), secure cloud/databricks integrations, APIs/microservices, monitor model performance, and mentor engineers while ensuring compliance and operational excellence.

Lead the design and delivery of enterprise-grade AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable, secure cloud-native systems (AWS, Cloudera, Databricks), develop APIs and microservices, implement LLM frameworks (e.g., LangChain), optimize models in production, and collaborate across product, engineering, and data teams while ensuring governance, security, and compliance.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Build scalable cloud architectures (AWS, Cloudera, Databricks), APIs and microservices, integrate LLM frameworks (e.g., LangChain), monitor model performance, and ensure security/compliance. Mentor engineers, drive technical design, and collaborate with product and data teams to productionize AI capabilities.

Lead design and delivery of enterprise-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, LLM-driven applications (LangChain), and scalable cloud architectures (AWS, Cloudera, Databricks). Implement secure microservices/APIs, monitor model performance in production, mentor engineers, and ensure compliance with data governance and security standards.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and microservices; architect scalable cloud/data platforms (AWS, Cloudera, Databricks); implement LLM orchestration (LangChain, MCP); monitor and optimize production model performance; ensure data governance, security, and compliance; mentor engineers and contribute to technical design.

Lead design and deliver enterprise AI solutions using LLMs, RAG, and vector-search knowledge bases. Architect secure, scalable cloud/data systems (AWS, Cloudera, Databricks), build APIs and microservices, implement LangChain-based apps, optimize production model performance, and mentor engineers while ensuring data governance and regulatory compliance.

Designs, develops, tests, deploys, and operates AI-powered consumer support applications and services. Builds conversational experiences involving intent understanding, retrieval, response generation, orchestration, routing, and integrations. Develops evaluation and regression frameworks, improves reliability and performance, supports live-site operations, and uses telemetry and experimentation to address quality gaps. Collaborates with engineering, product, design, and support stakeholders while applying responsible AI, security, privacy, accessibility, and compliance practices.

Lead and deliver secure, scalable cloud-native software across Python/Node.js/React stack. Design, develop, test, and operate production services, mentor engineers, drive architecture and CI/CD/IaC standards, and champion responsible AI-assisted engineering practices and operational stability.

Lead the strategy, architecture, delivery, and operation of AWS-native platform services, infrastructure as code, CI/CD, developer tooling, and enterprise integrations across Atlassian, ServiceNow, Workday, and other SaaS platforms. Design secure, scalable cloud foundations and event-driven integrations, improve developer experience and operational quality, establish governance and observability, and mentor a global team while partnering with technology, cybersecurity, and business stakeholders.

Lead and mentor a team of software engineers building engineering and data products for Windows. Set technical direction, drive AI/big-data solutions, partner across product and program teams, and promote engineering excellence, reliability, and strong user outcomes.

Build privacy-first analytics capabilities for Matomo, developing backend PHP and frontend JavaScript features, prototypes, AI-powered insights, visualizations, integrations, and scalable product innovations. Own solutions from concept through deployment, write automated tests, review code, document technical work, and ensure secure implementation. Collaborate autonomously with the CTO, senior engineers, and global teams while balancing rapid delivery, usability, scalability, and long-term product value.

Design, implement, and ship engineering and data products that transform how Windows is built and delivered. Collaborate across engineering and product teams, leverage AI and big-data solutions, influence technical direction, mentor junior engineers, and promote engineering excellence, reliability, and great user experiences.

Designs, builds, and maintains scalable Azure data pipelines and platform architecture. Responsibilities include developing Synapse pipelines, creating data models, optimizing databases, deploying through Azure DevOps and Terraform, implementing data quality and monitoring controls, supporting governance and security, troubleshooting environments, and sharing platform knowledge. The role also requires collaboration with analysts, infrastructure teams, technical stakeholders, and users while keeping current with Microsoft Fabric and migration approaches.

Lead enterprise customer engagements to define agentic AI strategies, architect Salesforce-based solutions, oversee deployments, coordinate delivery teams, manage stakeholders, and drive adoption and measurable business value. The role combines AI readiness assessment, change management, responsible AI practices, program execution, customer advisory, and product feedback. Engagements typically last 12 weeks or less and may span multiple strategic customers.

Build and maintain backend services, APIs, data schemas, and scalable full-stack features for a multi-tenant SaaS cash forecasting platform. Own PostgreSQL data integrity and query performance, testing with RSpec, Docker deployments, AWS infrastructure, automation, observability, and performance optimization. Collaborate with product, design, and engineering teams, participate in code reviews and technical design, and join the on-call rotation.

Oversee European ETF and ETN product operations, including pricing, NAVs, collateral, market data, tracking error, and counterparty risk. Lead new product launches, regulatory changes, process improvements, and business analysis initiatives. Support product structuring, manage service-provider relationships, resolve complex operational issues, and improve scalability through automation, technology, and AI-enabled workflows.

Build and operate RenaissanceRe’s internal AI platform, including reusable tools, cloud-native deployments, enterprise integrations, monitoring, governance, testing, and responsible-AI controls. Develop AI agents, RAG frameworks, tool-calling workflows, and scalable services using Python, AWS, Docker, and orchestration frameworks. Support security, privacy, compliance, performance, reliability, and cost optimization while partnering with product, data, platform, and engineering teams to accelerate AI adoption.

Delivers client-facing advisory projects for charities and not-for-profit organizations, covering financial modeling, strategic planning, governance, sustainability, data analysis, and project management. Produces reports, presentations, dashboards, and recommendations while improving internal workflows through automation, AI, process standardization, and quality assurance. Coordinates international stakeholders, manages project workstreams, supports client engagements, and may mentor junior colleagues.

The Software Engineer for Data Infrastructure & Acquisition will enhance data collection for AI model training, improve ingestion pipelines, and collaborate with AI scientists for optimal data usage.

Support cloud-hosted application testing, implementation, maintenance, and validation. Monitor and troubleshoot Windows, Linux, server, network, and application environments. Assist with incident response, documentation, and operational improvements. Contribute to automation, deployments, and on-call support.

Provide expert technical support and advisory to large MongoDB customers on architecture, performance, recovery, and security. Advocate customer needs to product teams, contribute to internal tooling and diagnostics, and operate across the MongoDB ecosystem including Atlas and Cloud Manager.

As the Director of Data Science, you'll mentor teams, oversee projects, drive data-driven decisions, and implement AI and ML solutions while enhancing our data pipeline.