Top Python Jobs in Dublin

Operate and improve production platform reliability: participate in on-call and incident response, manage Kubernetes and cloud infrastructure, enhance observability, automate toil, maintain runbooks, and collaborate with engineering teams to reduce incidents and improve operational readiness.

Embed with enterprise health customers to deploy and maintain Heidi: own end-to-end onboarding (EMR integrations, SSO/SCIM, data pipelines), build custom clinical workflows in code, debug production issues, automate deployments, train customers, and feed field insights to Product and Engineering.

Design, deploy, and scale low-latency inference infrastructure for generative audio models (TTS, ASR, voice conversion). Build high-performance inference engines, Kubernetes-based autoscaling, CI/CD pipelines, observability, and GPU optimization to bridge research and production for streaming and batch workloads.

The role involves delivering features across the CRM platform for distributors, using AI tools, handling fullstack development tasks, and collaborating closely with teams and customers.

Develop and enhance features for EnrollWise, contribute to a scalable software architecture, review code, collaborate on design and technical feasibility, and maintain software quality.

Embed with Mimir, Loki, and Tempo squads to own production reliability for high-SLA Grafana Cloud database customers. Design SLOs, build automation to scale reliability, reduce SLO burn, lead incident response and post-incident reviews, improve observability and alerting, mentor engineers, influence product design for operability, and drive toil elimination and scalable operational practices across multi-cloud, multi-tenant environments.

Provide fast-paced technical support for DataVisor's fraud prevention SaaS platform (Wednesday–Sunday schedule). Triage and resolve customer tickets via Zendesk, monitor system health and alerts, collaborate with engineering for root-cause analysis, document findings in Jira, analyze recurring issues, and feed product team with usability and enhancement feedback.

Design, implement, and productionize Databricks and ADF data pipelines; develop and version data models; manage Azure cloud data infrastructure; execute schema and data migrations; optimize ETL performance and complex data flows; support DevOps practices and collaborate on testing and deployment. Power BI experience is a plus.

As a Senior Atlassian Platform Engineer, you will administer and support Jira and Confluence platforms, automate tasks, troubleshoot issues, and collaborate with various teams to enhance performance and stability.

Build and own a multi-tenant platform connecting enterprise customers and sovereign deployments to inference infrastructure. Responsibilities include authentication, tenant isolation, quotas, rate limiting, usage metering, entitlements, public inference APIs, SSO, access controls, audit logs, data residency, and security testing. The role requires strong Python and backend or platform engineering expertise, API design experience, and familiarity with billing, credits, reconciliation, regulated environments, and enterprise identity systems.

Own and optimize the cost, throughput, latency, and reliability of an AI inference stack. Responsibilities include improving KV-cache management, batching, speculative decoding, quantization, routing, and long-context workloads; tuning serving engines; and building profiling systems. The role requires production experience with inference infrastructure, GPU performance, model serving, and systems programming.

Provide hands-on IT support across macOS and other endpoints, troubleshoot hardware/software/networking/accounts, manage onboarding/offboarding and device enrollment, maintain device management and identity systems, join on-call rotation, write automation scripts (Bash/Python), document runbooks, and identify automation opportunities to improve scale.

Design, develop, and maintain high-quality software for automated document-processing products. Collaborate with stakeholders to translate business needs into product improvements, contribute to architecture and technology decisions, conduct code reviews, optimize scalability and performance, lead major technical initiatives, mentor teammates, and improve engineering practices.

Lead a one-month migration from an externally managed Microsoft Intune environment to an in-house MDM/UEM platform. Configure enrollment, security, compliance, and device policies across Windows, macOS, iOS, and Android; integrate identity systems; migrate and re-enroll devices; troubleshoot issues; and document the final environment for internal handover.

Design and maintain ETL/ELT pipelines and data warehouse architecture, analyze and visualize data with SQL/Python/R and BI tools, build machine learning models and A/B tests, and partner with engineering and business teams to deliver data-driven insights for fintech use cases (fraud detection, segmentation, forecasting).

Design, develop, test, deploy, and maintain scalable cloud-native software solutions. Collaborate with product and UX teams, troubleshoot complex production issues, conduct root cause analysis, and improve system performance. Lead small engineering groups through mentorship and technical guidance, implement automation and telemetry for CI/CD, monitor applications, evaluate changes, and support business users with sustainable technical solutions.

Leads development of secure automation and platform features for global client platforms. Builds LLM-enabled agentic workflows using tool calling, prompt evaluation, Graph and REST APIs, and Intune/Jamf integrations. Designs scalable systems, implements GitOps and infrastructure-as-code delivery, writes tests, manages secure auditability, monitors performance, and supports operational excellence. The role also includes code reviews, mentoring, and continuous quality improvement.

Develop and deploy production-ready generative AI applications using Python, LLMs, RAG, AWS Bedrock, and OpenAI APIs. Responsibilities include model customization, API integration, data preparation, performance tuning, production deployment, documentation, and cross-functional collaboration. The role focuses on applied engineering rather than research and requires experience integrating AI solutions into business applications.

Review and analyze variant reports and raw NGS/WGS data, investigate and classify unsubmitted ClinVar variants, link unclassified variants to conditions, support customer troubleshooting, and help refine AI prompts and classification methodologies.

Lead design and ownership of a cloud-native data platform: build scalable batch ETL and ingestion pipelines, develop analytical models (Athena/Redshift/ClickHouse), integrate data workflows with microservices on ECS, ensure data quality/observability, performance tuning, implement IaC and CI/CD, and mentor engineers.

Leads end-to-end delivery of scalable software features and models, including design, development, testing, debugging, code review, CI/CD, performance monitoring, and maintenance. Integrates AI/ML components, translates business needs into technical requirements, collaborates with stakeholders, mentors engineers, and advances engineering practices. The role uses Java, Python, Kafka, Kubernetes, Airflow, and Salesforce within Walmart’s AdTech platform.

Own and develop the company’s production Content Operating System across FastAPI services, React front ends, PostgreSQL, and Google Cloud Run. Responsibilities include shipping features, managing sprint commitments, testing and releases, monitoring reliability, responding to incidents, making architecture decisions, managing technical debt, and directing AI coding agents. The role requires close collaboration with an AI Developer in a small, remote, async technical team.

Design, maintain, and improve CI/CD pipelines, release automation, and cloud infrastructure. Automate deployments using Docker, Terraform, Ansible, and Kubernetes. Support and troubleshoot hybrid cloud and on-prem systems (including Azure HCI), optimize processes, and collaborate with development and QA teams to increase reliability and operational efficiency.

Build scalable backend platform capabilities, infrastructure, tooling, and ML frameworks for Walmart’s Membership Experience Engineering team. Lead the full software development lifecycle, apply cloud-native architecture, design APIs and resilient systems, integrate AI and machine learning components, conduct design reviews, and ensure deployment, observability, and maintenance. Mentor engineers, influence technical decisions, collaborate with stakeholders, and use data-driven practices to improve engineering productivity and product outcomes.

Lead reliability, observability, and infrastructure strategy for Remote Build. Design Terraform and Kubernetes IaC, run production K8s on AWS, build monitoring/incident response, embed security and compliance, optimize performance/costs, automate toil, and improve platform developer experience.