Top Python Jobs in Dublin

Own fraud detection and prevention for a product by analyzing large datasets, identifying fraud patterns, creating risk rules, optimizing false-positive rates and 3DS decisions, and improving verification workflows. Collaborate with Product, Payments, Data, and Engineering teams while tracking fraud trends and communicating data-driven recommendations. Strong SQL and practical payments-risk expertise are required; Python and fintech experience are preferred.

Build and maintain data pipelines and infrastructure for analytics, experimentation, and company metrics. Collaborate with machine learning engineers on training, quality assessment, and model release automation. Improve data delivery processes, automate manual workflows, and develop scalable data models. The role requires strong Apache Spark and Python expertise, SQL proficiency, Git experience, and familiarity with cloud data platforms and Kubernetes.

Build and maintain data pipelines, infrastructure, data models, and automation for analytics and machine learning workflows. Collaborate with ML engineers on model training, quality assessment, and releases. Improve internal data delivery processes and enable efficient access to large-scale organizational data.

Lead research and production development of LLMs and transformer models for machine translation and language quality. Design evaluation frameworks, agentic NLP workflows, training pipelines, and model-provider strategies. Own flagship model programs from data curation through production integration, mentor researchers, review technical work, and collaborate with Product and Engineering to ship capabilities. The role requires deep NLP and LLM expertise, rigorous experimentation, strong Python and ML library skills, and technical leadership.

Own production optimization of LLM and VLM inference systems, improving latency, throughput, memory efficiency, GPU utilization, quality, reliability, and cost. Deploy and benchmark inference engines, build compression and quantization workflows, implement acceleration techniques, diagnose serving bottlenecks, and collaborate with kernel, platform, research, product, and customer teams.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production infrastructure. Build smart payment-routing models and MLOps practices, partner with product, data, engineering, and customers, evaluate outcomes statistically, mentor engineers, and help define ML technical direction and reliable development processes.

Own the full lifecycle of machine learning features, including research, experimentation, training, deployment, monitoring, and production optimization. Build ML infrastructure for payment routing, establish reliable MLOps practices, partner with product, data, engineering, and customers, mentor engineers, and guide technical priorities. Apply statistical thinking, A/B testing, pragmatic model evaluation, and reinforcement learning concepts in a remote-first environment.

Architects and operates highly available Linux infrastructure and core IT services across on-premises and cloud environments. Develops infrastructure-as-code and automation with Terraform, Ansible, Git, Python, and shell scripting; implements secure configuration, patching, and deployment pipelines; administers enterprise storage arrays; and supports networking, virtualization, Kubernetes, compliance, and on-call operations. Acts as a technical subject matter expert, maintains documentation and runbooks, and mentors junior engineers.

Designs and builds end-to-end automation solutions for Lilly’s global Finance organization. Responsibilities include solution design, programming, architecture documentation, testing, UAT support, validation, defect resolution, risk communication, QA, audits, and platform migrations. The role uses Python, PySpark, TypeScript, PostgreSQL, AWS, Microsoft Fabric, SAP, APIs, and emerging AI technologies while partnering with analysts, business teams, and technology stakeholders.

Designs, deploys, operates, and troubleshoots large-scale datacenter, campus, core, and high-performance compute networks. Owns routing and switching standards, qualifies platforms and topologies, improves monitoring and documentation, automates network tasks, resolves Layer 2/Layer 3 incidents, and supports capacity build-outs. The role partners with compute, facilities, and software teams, participates in on-call rotations, and may require travel to build sites.

Leads global longevity reinsurance pricing referrals and business acceptance reviews. Evaluates best estimates, pricing models, deal structures, and expert judgments while coordinating with global and local pricing teams. Supports assumption governance, methodology implementation, cross-reviews, and referral community engagement. Participates in client meetings, due diligence, and negotiations, while sharing best practices and guiding complex transactions across international markets.

Lead design and implement reliability, scalability, and observability for cloud and data platforms. Build IaC, CI/CD pipelines, use monitoring and SLO-driven improvements, apply enterprise AI for incident triage and reliability insights, and collaborate across teams to reduce toil and improve operational outcomes.

Design, build, and maintain secure, scalable employee-facing software and AI-enabled tooling. Lead implementation of AI-assisted engineering practices, collaborate cross-functionally, apply CI/CD and cloud-native patterns (preferably AWS), and ensure resiliency, security, and validation standards across the SDLC.

Lead and mentor SRE practices to design, implement, and operate reliable cloud and data platforms. Build IaC, CI/CD, observability, and containerized deployments; use enterprise AI to accelerate incident triage and reliability improvements while ensuring compliance and data sensitivity controls.

Lead design and build production software for enterprise-scale reliability: automation, control loops, self-healing, SLI/SLO definition and alerting, observability, incident leadership, on-call ownership, toil reduction, and AI-native engineering across the SDLC while ensuring security, performance, and cost control.

Lead and coach data engineering teams to onboard enterprise datasets into Palmos and deliver scalable Databricks/Spark pipelines. Drive adoption of data mesh and lakehouse patterns, enforce governance, security, observability, and SDLC best practices, and enable analytics and AI/ML use cases through high-quality, reusable data products.

Lead development of secure, scalable HR/talent-management backend systems on AWS. Design, code, review, and troubleshoot production software; build IaC and data models; drive CI/CD, resiliency, automation, and responsible AI-assisted engineering adoption; lead vendor evaluations and engineering communities of practice.

Build end-to-end machine learning systems spanning research reproduction, model evaluation, inference infrastructure, backend services, and product interfaces. Design rigorous experiments, evaluation datasets, and verification workflows; analyze model internals; investigate provenance and evasion; and convert findings into reliable production systems with APIs, background jobs, observability, testing, documentation, and user-facing React/TypeScript experiences.

Build and scale Base360.ai’s short-term rental platform across frontend, backend, integrations, automation, dashboards, and AI-powered workflows. Work directly with customers, founders, and designers to identify problems, ship end-to-end solutions, and improve product outcomes. Develop reliable APIs, event-driven automations, cloud infrastructure, and integrations with Airbnb, Stripe, Twilio, OpenAI, and other platforms. Own features from discovery through deployment while balancing usability, security, scalability, and maintainability.

Build and scale Base360.ai’s short-term rental platform across frontend, backend, integrations, automation, AI workflows, dashboards, and customer-facing features. Own products end to end, from understanding customer problems through deployment and iteration. Develop reliable APIs and integrations with Airbnb, Stripe, Twilio, OpenAI, and other partners while improving usability, scalability, security, and operational outcomes.

Build and scale Base360.ai’s short-term rental platform, including customer-facing applications, APIs, automation engines, AI-powered workflows, third-party integrations, and cloud infrastructure. Develop systems for reservations, pricing, payments, guest communication, property operations, reporting, and financial workflows. Own projects end to end, make architectural decisions, ensure reliability, and deliver scalable products in a remote-first startup environment.

Establishes SRE practices across the platform organization, including SLO/SLI frameworks, incident response, observability, and reliability standards. Builds reusable Go services, CLIs, Terraform modules, Cloud Run services, and GKE workloads. Drives GCP managed-service adoption, security, networking, compliance, and developer self-service patterns while mentoring engineers and participating in on-call.

The Quantitative Strategist role involves building predictive models, developing trading strategies, analyzing risks, and automating research tasks in a collaborative financial firm environment.

The role involves developing and managing distributed storage systems, ensuring reliability, and optimizing infrastructure performance. Responsibilities include defining SLOs and participating in an on-call rotation.

Own the production improvement loop for Elastic’s Context Engine, improving AI agents, automations, retrieval, memory, telemetry, and evaluation. Design safe rollout processes, regression coverage, experiments, and quality, latency, and cost evaluators. Build production code in TypeScript or Python, collaborate with data science and product teams, mentor engineers, review designs, and influence the roadmap for agentic behavior and enterprise AI products.