Top Python Jobs in Dublin

Design and deploy system-level sensing and grid-monitoring solutions by engaging customers, prototyping hardware, integrating embedded software and analytics, troubleshooting cross-domain issues, validating systems to standards, and supporting field trials and production deployments.

Design, build and maintain Splunk dashboards and clusters: ingest Big Data, create SPL, indexes, lookups, forwarders, and CI tests. Support, monitor, upgrade clusters, script cron jobs, analyze performance data, and collaborate with global teams.

Lead design and delivery of Databricks-based banking data platforms on AWS. Build and optimise large-scale Spark/PySpark pipelines with Delta Lake, own data governance (Unity Catalog), manage Terraform IaC, drive CI/CD, mentor engineers, maintain documentation, and ensure scalable, secure, cost-effective architectures in a regulated financial environment.

Lead the EMEA Cloud Platform organization, serve as senior reliability partner to customers, drive platform reliability/observability, define roadmap and automation, manage and develop distributed engineering teams, and represent the platform to executive customers.

Design, build and maintain scalable Azure-based data pipelines and platform architecture. Develop Azure Synapse pipelines, implement data models, performance tuning, data quality controls, and DevOps deployments (Azure DevOps/Terraform). Work with stakeholders and infrastructure teams, ensure governance/GDPR compliance, deliver user enablement, and stay current with Microsoft data technologies including Microsoft Fabric.

Support and maintain Azure environments: monitor performance, troubleshoot incidents, implement security and backup solutions, automate deployments with IaC and scripting, manage networking and identity services, support DevOps pipelines and integrations, and produce technical documentation.

Design, build, and operate scalable control-plane services and Kubernetes controllers enforcing network segmentation and security policies across clusters, clouds, and data centers. Integrate cloud-native networking (CNI, service mesh), automate policy lifecycle, lead production troubleshooting, apply CI/CD and observability, mentor engineers, and use AI tools to accelerate development and operations.

Design, build, and maintain scalable backend services for eBay's Cloud Security Segmentation platform. Develop Kubernetes-based control plane services, troubleshoot distributed systems and cloud infrastructure, use CI/CD and automated testing, participate in code reviews and incident response, and leverage AI-assisted engineering tools to improve productivity and reliability.

Build and maintain hardened Ubuntu-based container images and the automated CI/CD pipelines to build, test, scan, sign, publish, and retire them. Integrate upstream open-source projects, ensure security and cloud-native compatibility, collaborate with cross-functional teams, document and share knowledge, mentor colleagues, and contribute to hiring. Work in a distributed agile environment with occasional travel.

Ownership of platform reliability and incident response across cloud infrastructure. Improve observability, CI/CD pipelines, deployments, developer experience, and operational tooling. Act as primary on-call platform expert, collaborate with product teams, and run postmortems to raise platform reliability.

Lead a cross-functional personalization team building production ML systems for search and browse. Own model design, user representations, embeddings, segmentation, offline evaluation, experimentation, and metrics-driven iteration. Hire, mentor, partner with product/engineering, and contribute technically to architecture and implementation to drive measurable retailer KPI improvements.

Build and deploy production ML systems for large-scale ecommerce search and recommendations using text, image, and multimodal approaches. Tune and integrate LLMs and transformer models, develop end-to-end visual solutions, improve business KPIs through experimentation, and collaborate across teams to productize and scale ML capabilities.

Analyze large-scale user and product data, design and evaluate A/B tests, define and validate product and business metrics, build dashboards and reports, and partner with engineers, data scientists, product, and customers to translate insights into product improvements.

Analyze and instrument offsite discovery and SEO/GEO performance for ecommerce sites. Build integrations, ETL pipelines, and models to track organic and AI-driven traffic. Create tooling and frameworks for technical site audits, landing page modelling, SEO experiments, and enablement for product and customer teams.

Lead and scale the Offsite Discovery engineering team to build a high-performance, cost-efficient real-time personalization platform. Set engineering strategy, partner with product, hire and mentor engineers, define metrics and alerting, improve operational excellence, and collaborate across teams to drive measurable improvements in performance, cost, and product capabilities.

Design, build, deploy, and maintain high-load, real-time ML-based ranking services and a Ranking Platform. Improve performance, feature delivery (feature store), CI/CD, and collaborate with DS, MLEs, and stakeholders.

Design, deliver, and maintain high‑load real‑time recommendation web services. Implement IaC, CI/CD, observability, and stability testing. Collaborate with cross‑functional teams, integrate NoSQL/SQL and external services, and migrate features into distributed services.

Design, build, and maintain scalable backend services that deliver ML-enriched product attributes and badges. Develop high-throughput, low-latency APIs and manage a dedicated database, CI/CD pipelines, observability, and tests. Collaborate with ML engineers and cross-functional teams to ensure reliability, performance, and availability for downstream systems and customer dashboards.

Design, build, and optimize high-throughput, low-latency internal platform services (e.g., bot detection, rate limiting, traffic priority). Collaborate with product teams to deliver resilient, secure, code-driven solutions atop cloud infrastructure, using containers, CI/CD, and infrastructure-as-code.

Build, deliver, and maintain a customer dashboard using TypeScript and React that interacts with Python/FastAPI backend services. Collaborate with cross-functional teams, design APIs, ensure high-scale performance, analyze dashboard metrics using SQL, and contribute product ideas and stakeholder communications.

Build and operate scalable MLOps pipelines, model serving, and inference architectures. Implement CI/CD, monitoring, A/B/canary deployments, feature stores, GPU autoscaling, and secure governance for production ML systems.

Build and maintain Python services that ingest, normalize, validate, and distribute real-time and batch sports data. Integrate third-party feeds, improve data quality, monitoring, replay tooling, and observability. Troubleshoot production issues, contribute to architecture and standards, and collaborate across product, trading, QA, and platform teams.

Own and deliver backend features for integrations using Python and Django. Design, build, and maintain integrations with KYC, geolocation, payment partners and third-party services. Implement observability (monitoring, logging, alerting), write unit and integration tests, support production incidents, and participate in on-call rotations while collaborating with product and engineering teams.

Provide expert-level technical support for a cloud-native security platform across AWS, Azure, GCP and OCI. Troubleshoot cloud connectors, Kubernetes and runtime security, analyze vulnerabilities and SBOM/SCA results, support shift-left CI/CD/IaC workflows, design AI-driven automation, and participate in on-call rotations.

Lead applied research and engineering to build production-grade video foundation models. Work includes developing latent video diffusion models, designing conditioning for controllability, scaling distributed multi-GPU training, improving training stability, building evaluation frameworks, and optimizing inference for low-latency, high-resolution deployment.