Top Python Jobs in Dublin

Lead a team of AI engineers to develop, fine-tune, and deploy foundation models and generative AI solutions. Drive end-to-end delivery of use cases, partner with product and data teams for scalable deployments, establish evaluation and responsible AI practices, and provide technical coaching and delivery oversight to meet program priorities.

Lead a team building and deploying foundation models and generative AI solutions. Drive end-to-end delivery from problem definition to production, partner with product and data teams, establish evaluation and governance practices, embed responsible AI, and provide technical leadership and coaching to ensure scalable, reliable, measurable AI use cases.

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.

Design and deliver enterprise-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, APIs, and microservices; deploy scalable, secure systems on cloud platforms (AWS, Cloudera, Databricks). Collaborate cross-functionally, monitor model performance, ensure data governance and compliance, and mentor junior engineers.

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.

Lead an AI engineering team to develop, fine-tune, and deploy foundation models (transformer/generative). Drive end-to-end delivery of high-impact AI use cases, partner with product and data teams, ensure MLOps, evaluation, performance optimization, and responsible AI practices, and provide technical coaching and delivery oversight.

Lead modernization of Fraud Rules/Engines and large-scale migrations. Provide technical leadership across cross-functional teams, ensure architectural compliance, design and operate scalable, low-latency global platforms, mentor developers, scope and estimate projects, and incorporate AI/LLM technologies for fraud solutions.

Design and deliver enterprise AI solutions using LLMs and RAG; build and maintain vector-search knowledge bases; develop LangChain-driven applications; architect secure, scalable cloud/data systems (AWS, Cloudera, Databricks); implement APIs and microservices; monitor model performance; mentor engineers and ensure data governance and security compliance.

Lead modernization of fraud-rule and engine platforms, drive large-scale migrations, provide technical leadership across architecture, design and implementation, mentor engineers, ensure security/compliance, build highly available low-latency microservice platforms, and incorporate AI/LLM relevant to fraud detection.

Design and deliver enterprise-grade AI solutions using LLMs, RAG, and vector search. Build knowledge bases, integrate models via MCP/LangChain, develop APIs and microservices, and architect scalable, secure cloud systems on AWS/Cloudera/Databricks. Collaborate cross-functionally, monitor model performance in production, ensure compliance and data governance, and mentor junior engineers.

Lead a team building and deploying foundation models and generative AI solutions. Own end-to-end delivery from problem definition to production, collaborate with product and data teams, establish evaluation and MLOps practices, embed responsible AI, and coach engineers to ensure reliable, secure, and scalable AI deployments aligned to business priorities.

Lead modernization of fraud-rule platforms and large-scale migrations. Provide technical leadership across cross-functional teams, design scalable low-latency microservices, mentor engineers, ensure architecture, security, and compliance, and apply GenAI/LLM familiarity to fraud solutions.

Lead modernization of fraud-rule platforms and large-scale migrations. Provide technical leadership across cross-functional teams, design compliant scalable microservice architectures, mentor engineers, scope and estimate projects, and ensure high-throughput, low-latency global platform operations. Apply multi-language development (Go, Python, Java), DevOps practices, and familiarity with GenAI/LLMs for fraud rules.

Lead a team building and deploying foundation models and generative AI solutions. Drive end-to-end delivery from problem definition through fine-tuning, evaluation, integration, and production deployment. Partner with product, data engineering, and platform teams, embed responsible AI practices, set evaluation and experimentation standards, and provide technical leadership and coaching to ensure reliable, secure, measurable AI use cases.

The Technical Account Manager provides hands-on technical support and guidance to Datadog's customers, focusing on product adoption, IT Operations analysis, and maximizing customer satisfaction.

Design, build, and deploy production AI solutions including transformer-based and generative models. Build data pipelines, training workflows, model serving and inference, apply MLOps practices, collaborate with product and engineering teams, and monitor and tune models in production.

Design, build, and deploy production AI/ML solutions including transformer and generative models. Implement data pipelines, feature engineering, training workflows, model serving, and MLOps practices. Collaborate with product and engineering teams, participate in code reviews, troubleshoot and optimize models in production.

Design, build, and deploy production AI/ML solutions, focusing on transformer and generative models. Develop data pipelines, training workflows, model serving, and MLOps practices. Collaborate with product, data, and engineering teams to ensure reliable, monitored, high-quality model performance.

Design, build, and deploy production AI solutions including transformer and generative models; develop data pipelines and feature engineering; implement model serving, monitoring, and MLOps practices; collaborate with product and engineering teams to ensure reliable, performant systems.

Design, build, and deploy production-quality AI solutions. Develop and optimize transformer and generative models, manage data pipelines and training workflows, implement model serving and MLOps practices, and collaborate with product and engineering teams to operationalize and monitor AI systems.

Design, build, and deploy production AI/ML solutions including transformer and generative models. Develop data pipelines, training workflows, model serving, and MLOps practices. Collaborate with product and engineering teams, perform evaluation and performance tuning, and ensure maintainable, production-grade AI systems.

Design, build, and deploy transformer-based and generative AI models into production. Build data pipelines and feature engineering, implement model serving and inference, apply MLOps practices (tracking, versioning, testing, monitoring), collaborate with product and engineering teams, and troubleshoot and optimize model performance in production.